{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Global Mean Response\n", "* Wenchang Yang (wenchang@princeton.edu)\n", "* Department of Geosciences, Princeton University" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## import and parameters" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-07-12T20:45:19.712462Z", "start_time": "2018-07-12T20:45:19.513662Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thu Jul 12 16:45:19 EDT 2018\n", "[Start] matplotlib settings: ####################\n", "Python: 3.6.5\n", "matplotlib: 2.2.2 backend nbAgg\n", "interactive = True\n", "figure.max_open_warning = 50\n", "hatch.linewidth = 0.5\n", "hatch.color = gray\n", "legend.frameon = False\n", "savefig.bbox = tight\n", "savefig.format = pdf\n", "[End] matplotlib settings. ####################\n" ] } ], "source": [ "%matplotlib notebook\n", "!date\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# plt.rcParams['hatch.color']='g'\n", "import xarray as xr\n", "from scipy.stats import ttest_1samp\n", "\n", "from plotsetting import *\n", "from geoplots import mapplot, xticksyear, xticksmonth\n", "import geoxarray" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-07-12T21:02:46.171527Z", "start_time": "2018-07-12T21:02:46.002677Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tigress/wenchang/miniconda3/lib/python3.6/site-packages/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range\n", " enable_cftimeindex)\n", "/tigress/wenchang/miniconda3/lib/python3.6/site-packages/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range\n", " return self.func(self.array[key])\n" ] } ], "source": [ "# parameters\n", "data_names = ['swup_toa_clr', 'netrad_toa', 't_surf', 'sfc_hflux_coupler']\n", "figname = f'figs/fig_globalMean.pdf'\n", "prcp_scale = 3600 * 24\n", "\n", "ds = xr.open_dataset('/tigress/wenchang/MODEL_OUT/PIctl_CMIP6volc/POSTP/00010101.ocean_grid.nc')\n", "ocean_tgrid_area = ds.area_t + ds.ht*0 # ds.ht*0 is the ocean mask\n", "\n", "# global mean for ocean data\n", "def area_mean(da, yt_ocean=None):\n", " '''area-weighted average for ocean data'''\n", " if yt_ocean is None:\n", " da_ = da\n", " area = ocean_tgrid_area\n", " else:\n", " da_ = da.sel(yt_ocean=yt_ocean)\n", " area = ocean_tgrid_area.sel(yt_ocean=yt_ocean)\n", " return (da * area).sum(['xt_ocean', 'yt_ocean'])/area.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agung data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-07-12T21:07:54.109468Z", "start_time": "2018-07-12T21:06:45.038568Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.swup_toa_clr.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.swup_toa_clr.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.netrad_toa.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.netrad_toa.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.t_surf.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.t_surf.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.sfc_hflux_coupler.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_agung.ipynb.sfc_hflux_coupler.nc\n" ] } ], "source": [ "# Agung data\n", "tsas_agung = dict()\n", "for data_name in data_names:\n", " from data_CTL1860 import open_ensemble as get_ctl\n", " from data_agung import open_data as get_volcano\n", " volcano_name = 'Agung'\n", " year_volcano = 1963\n", " ens = range(1,31)\n", " nbname = 'volcano_impact_on_climate_agung.ipynb'\n", " new_names = {'grid_xt': 'lon', 'grid_yt': 'lat'}\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.ctl.nc'\n", " try:\n", " da_ctl = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_ctl = get_ctl(data_name, ens=ens, year_volcano=year_volcano).rename(new_names).load()\n", " da_ctl.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.nc'\n", " try:\n", " da_volcano = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_volcano = get_volcano(data_name, ens=ens).rename(new_names).load()\n", " da_volcano.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " daa = da_volcano - da_ctl\n", " if data_name in ('precip',):\n", " daa = daa * prcp_scale\n", " \n", " tsas_agung[data_name] = dict()\n", " if data_name in ('sfc_hflux_coupler',): # area mean for ocean grids\n", " tsas_agung[data_name]['Global'] = area_mean(daa)\n", " tsas_agung[data_name]['NH'] = area_mean(daa, yt_ocean=slice(0,90))\n", " tsas_agung[data_name]['SH'] = area_mean(daa, yt_ocean=slice(-90,0))\n", " else:\n", " tsas_agung[data_name]['Global'] = daa.geo.fldmean()\n", " tsas_agung[data_name]['NH'] = daa.sel(lat=slice(0,90)).geo.fldmean()\n", " tsas_agung[data_name]['SH'] = daa.sel(lat=slice(-90,0)).geo.fldmean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## StMaria data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-07-12T21:09:47.775986Z", "start_time": "2018-07-12T21:08:37.018316Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.swup_toa_clr.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.swup_toa_clr.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.netrad_toa.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.netrad_toa.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.t_surf.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.t_surf.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.sfc_hflux_coupler.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_stmaria.ipynb.sfc_hflux_coupler.nc\n" ] } ], "source": [ "# St Maria data\n", "tsas_stmaria = dict()\n", "for data_name in data_names:\n", " from data_CTL1860 import open_ensemble as get_ctl\n", " from data_stmaria import open_data as get_volcano\n", " volcano_name = 'StMaria'\n", " year_volcano = 1902\n", " ens = range(1,30+1)\n", " nbname = 'volcano_impact_on_climate_stmaria.ipynb'\n", " new_names = {'grid_xt': 'lon', 'grid_yt': 'lat'}\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.ctl.nc'\n", " try:\n", " da_ctl = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_ctl = get_ctl(data_name, ens=ens, year_volcano=year_volcano).rename(new_names).load()\n", " da_ctl.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.nc'\n", " try:\n", " da_volcano = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_volcano = get_volcano(data_name, ens=ens).rename(new_names).load()\n", " da_volcano.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " daa = da_volcano - da_ctl\n", " if data_name in ('precip',):\n", " daa = daa * prcp_scale\n", " \n", " tsas_stmaria[data_name] = dict()\n", " if data_name in ('sfc_hflux_coupler',): # area mean for ocean grids\n", " tsas_stmaria[data_name]['Global'] = area_mean(daa)\n", " tsas_stmaria[data_name]['NH'] = area_mean(daa, yt_ocean=slice(0,90))\n", " tsas_stmaria[data_name]['SH'] = area_mean(daa, yt_ocean=slice(-90,0))\n", " else:\n", " tsas_stmaria[data_name]['Global'] = daa.geo.fldmean()\n", " tsas_stmaria[data_name]['NH'] = daa.sel(lat=slice(0,90)).geo.fldmean()\n", " tsas_stmaria[data_name]['SH'] = daa.sel(lat=slice(-90,0)).geo.fldmean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pinatubo data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-07-12T21:11:26.082699Z", "start_time": "2018-07-12T21:10:15.731417Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.swup_toa_clr.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.swup_toa_clr.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.netrad_toa.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.netrad_toa.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.t_surf.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.t_surf.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.sfc_hflux_coupler.ctl.nc\n", "Data loaded from cache/volcano_impact_on_climate_pinatubo.ipynb.sfc_hflux_coupler.nc\n" ] } ], "source": [ "# Pinatubo data\n", "tsas_pinatubo = dict()\n", "for data_name in data_names:\n", " from data_CTL1860 import open_ensemble as get_ctl\n", " from data_pinatubo import open_data as get_volcano\n", " volcano_name = 'Pinatubo'\n", " year_volcano = 1991\n", " ens = range(1,30+1)\n", " nbname = 'volcano_impact_on_climate_pinatubo.ipynb'\n", " new_names = {'grid_xt': 'lon', 'grid_yt': 'lat'}\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.ctl.nc'\n", " try:\n", " da_ctl = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_ctl = get_ctl(data_name, ens=ens, year_volcano=year_volcano).rename(new_names).load()\n", " da_ctl.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " ncfile = f'cache/{nbname}.{data_name}.nc'\n", " try:\n", " da_volcano = xr.open_dataarray(ncfile).load()\n", " print('Data loaded from', ncfile)\n", " except:\n", " da_volcano = get_volcano(data_name, ens=ens).rename(new_names).load()\n", " da_volcano.to_dataset().to_netcdf(ncfile)\n", " print('Data calculated and saved to', ncfile)\n", "\n", " daa = da_volcano - da_ctl\n", " if data_name in ('precip',):\n", " daa = daa * prcp_scale\n", " \n", " tsas_pinatubo[data_name] = dict()\n", " if data_name in ('sfc_hflux_coupler',): # area mean for ocean grids\n", " tsas_pinatubo[data_name]['Global'] = area_mean(daa)\n", " tsas_pinatubo[data_name]['NH'] = area_mean(daa, yt_ocean=slice(0,90))\n", " tsas_pinatubo[data_name]['SH'] = area_mean(daa, yt_ocean=slice(-90,0))\n", " else:\n", " tsas_pinatubo[data_name]['Global'] = daa.geo.fldmean()\n", " tsas_pinatubo[data_name]['NH'] = daa.sel(lat=slice(0,90)).geo.fldmean()\n", " tsas_pinatubo[data_name]['SH'] = daa.sel(lat=slice(-90,0)).geo.fldmean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2018-07-13T15:27:22.227133Z", "start_time": "2018-07-13T15:27:20.517193Z" }, "code_folding": [ 0 ], "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot\n", "fill_alpha = 0.2\n", "\n", "fig, axes = plt.subplots(4,3,figsize=(8,7), sharey='row', sharex='col')\n", "# ##########\n", "plt.sca(axes[0,0])\n", "data_name = 'swup_toa_clr'\n", "\n", "ts = tsas_stmaria[data_name]['Global']*(-1) # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_stmaria[data_name]['NH']*(-1) # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_stmaria[data_name]['SH']*(-1) # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.title('Santa Maria 1902', loc='center')\n", "plt.grid(True)\n", "plt.ylabel('R$^{s}_{TOA}$ CLR [W m$^{-2}$]')\n", "plt.xlabel('')\n", "plt.ylim(-9,3)\n", "plt.text(.01,.99, '(a)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "plt.legend(loc='lower right', frameon=True, fontsize='small')\n", "\n", "\n", "# ############\n", "plt.sca(axes[0,1])\n", "\n", "ts = tsas_agung[data_name]['Global']*(-1) # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_agung[data_name]['NH']*(-1) # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_agung[data_name]['SH']*(-1) # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.title('Agung 1963', loc='center')\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(b)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ############\n", "plt.sca(axes[0,2])\n", "\n", "ts = tsas_pinatubo[data_name]['Global']*(-1) # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_pinatubo[data_name]['NH']*(-1) # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_pinatubo[data_name]['SH']*(-1) # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.title('Pinatubo 1991', loc='center')\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(c)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ##########\n", "plt.sca(axes[1,0])\n", "data_name = 'netrad_toa'\n", "\n", "ts = tsas_stmaria[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_stmaria[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_stmaria[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.ylabel('R$^{net}_{TOA}$ [W m$^{-2}$]')\n", "plt.ylim(-6,4)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(d)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ############\n", "plt.sca(axes[1,1])\n", "\n", "ts = tsas_agung[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_agung[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "ts = tsas_agung[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(e)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ############\n", "plt.sca(axes[1,2])\n", "\n", "ts = tsas_pinatubo[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_pinatubo[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_pinatubo[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(f)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ##########\n", "plt.sca(axes[2,0])\n", "data_name = 't_surf'\n", "\n", "ts = tsas_stmaria[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_stmaria[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_stmaria[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.ylabel('T$_s$ [K]')\n", "plt.ylim(-1,.5)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(g)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ############\n", "plt.sca(axes[2,1])\n", "\n", "ts = tsas_agung[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_agung[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "ts = tsas_agung[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(h)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ############\n", "plt.sca(axes[2,2])\n", "\n", "ts = tsas_pinatubo[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_pinatubo[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_pinatubo[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.xlabel('')\n", "plt.text(.01,.99, '(i)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "\n", "# ##########\n", "plt.sca(axes[3,0])\n", "data_name = 'sfc_hflux_coupler'\n", "\n", "ts = tsas_stmaria[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_stmaria[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_stmaria[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.ylabel('OHU [W m$^{-2}$]')\n", "plt.ylim(-10,8)\n", "plt.text(.01,.99, '(j)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "plt.setp(plt.gca().get_xticklabels(), visible=True, rotation=0, \n", " horizontalalignment='left')\n", "\n", "# ############\n", "plt.sca(axes[3,1])\n", "\n", "ts = tsas_agung[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_agung[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "ts = tsas_agung[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.text(.01,.99, '(k)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "plt.setp(plt.gca().get_xticklabels(), visible=True, rotation=0, \n", " horizontalalignment='left')\n", "\n", "# ############\n", "plt.sca(axes[3,2])\n", "\n", "ts = tsas_pinatubo[data_name]['Global'] # global mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='Global')\n", "\n", "ts = tsas_pinatubo[data_name]['NH'] # NH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='NH')\n", "\n", "ts = tsas_pinatubo[data_name]['SH'] # SH mean\n", "plt.fill_between(ts.time.to_index().to_pydatetime(),\n", " ts.mean('en') - ts.std('en'),\n", " ts.mean('en') + ts.std('en'),\n", " alpha=fill_alpha)\n", "ts.mean('en').plot(label='SH')\n", "\n", "xticksmonth(range(1,13,12), fstr='%Y')\n", "plt.xlim(ts.time.isel(time=[0,-1]).to_index())\n", "plt.grid(True)\n", "plt.text(.01,.99, '(l)', transform=plt.gca().transAxes,\n", " ha='left', va='top', fontsize='large')\n", "plt.setp(plt.gca().get_xticklabels(), visible=True, rotation=0, \n", " horizontalalignment='left')\n", "\n", "plt.tight_layout(h_pad=1, w_pad=1)\n", "plt.savefig(figname)" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "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": "3.6.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "293px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }