{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Volcanic Forcings and Feedback\n", "* Wenchang Yang (wenchang@princeton.edu)\n", "* Department of Geoscience, Princeton University" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-04-05T18:13:35.865732Z", "start_time": "2019-04-05T18:13:04.798450Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "**2019-04-05T14:13:04.800614**\n", ">>> Importing Python 3.7.2 packages...\n", "[OK]: import sys, os, os.path, datetime, glob\n", "[OK]: import numpy as np-1.16.2\n", "[OK]: import matplotlib as mpl-3.0.3; backend: module://ipykernel.pylab.backend_inline\n", "[OK]: #---import matplotlib.pyplot as plt\n", "[OK]: #---from pylab import *\n", "[OK]: import xarray as xr-0.12.0\n", "[OK]: #---import netCDF4\n", "[OK]: #---import dask\n", "[OK]: #---import bottleneck\n", "[OK]: import pandas as pd-0.24.2\n", "[OK]: from mpl_toolkits.basemap import Basemap\n", " PROJ_LIB = /tigress/wenchang/miniconda3p7/share/proj\n", ">>>Import packages from Wenchang Yang (wython)...\n", "[OK]: import geoplots as gt\n", "[OK]: from geoplots import geoplot, fxyplot, mapplot, xticksyear\n", "[OK]: import geoxarray\n", "[OK]: import filter\n", "[OK]: import xlearn\n", "[OK]: import mysignal as sig\n", "**Done**\n" ] } ], "source": [ "%run -im pythonstartup" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-04-05T18:17:49.012647Z", "start_time": "2019-04-05T18:17:49.006362Z" } }, "outputs": [], "source": [ "from lib.util import year_shift\n", "import xlearn\n", "from mystats import p2t\n", "\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-09-06T15:28:11.557098Z", "start_time": "2018-09-06T15:28:11.554046Z" } }, "outputs": [], "source": [ "nino_ens = [1, 3, 4, 7, 9, 12, 17, 22, 26, 29]\n", "nina_ens = [2, 5, 6, 8, 10, 11, 16, 23, 27, 30]\n", "neut_ens = [13, 14, 15, 18, 19, 20, 21, 24, 25, 28]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-04-05T18:13:55.382342Z", "start_time": "2019-04-05T18:13:55.380007Z" } }, "outputs": [], "source": [ "if 'das' in globals() or 'das' in locals():\n", " pass\n", "else:\n", " das = dict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## fig: 3 years since eruption" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-04-05T18:42:51.315483Z", "start_time": "2019-04-05T18:42:16.869219Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t_surf_Pinatubo\n", "t_surf_Agung\n", "t_surf_StMaria\n", "t_surf_Pinatubo_ctl\n", "t_surf_Agung_ctl\n", "t_surf_StMaria_ctl\n", "precip_Pinatubo\n", "precip_Agung\n", "precip_StMaria\n", "precip_Pinatubo_ctl\n", "precip_Agung_ctl\n", "precip_StMaria_ctl\n" ] } ], "source": [ "# data\n", "datanames = ['t_surf', 'precip']\n", "volcs = ['Pinatubo', 'Agung', 'StMaria']\n", "years = [1991, 1963, 1902]\n", "months = [6, 3, 10]\n", "n_years = 3\n", "\n", "for dataname in datanames:\n", " # volc\n", " ifiles = [f'data/{volc}_PI_ens_noleap.atmos_month.{dataname}.nc'\n", " for volc in volcs]\n", " for volc, ifile, yyyy, mm in zip(volcs, ifiles, years, months):\n", " key = f'{dataname}_{volc}'\n", " print(key)\n", " tspan = slice(f'{yyyy}-{mm:02d}', f'{yyyy+n_years}-{mm-1:02d}')\n", " da = xr.open_dataarray(ifile).sel(time=tspan).mean('time')\n", " das[key] = da\n", "\n", " # # volc_nudge\n", " # ifiles = [f'data/{volc}_ens_noleap_nudgeclimo_all_model1860.atmos_month.{dataname}.nc'\n", " # for volc in volcs]\n", " # for volc, ifile, yyyy, mm in zip(volcs, ifiles, years, months):\n", " # key = f'{volc}_nudge_{dataname}'\n", " # print(key)\n", " # tspan = slice(f'{yyyy+1}-{mm:02d}', f'{yyyy+1+n_years}-{mm-1:02d}')\n", " # da = xr.open_dataarray(ifile).sel(time=tspan).mean('time')\n", " # das[key] = da\n", "\n", " # ctl\n", " ifile = f'data/CTL1860_noleap_tigercpu_intelmpi_18_576PE.atmos_month.{dataname}.nc'\n", " for volc, yyyy, mm in zip(volcs, years, months):\n", " key = f'{dataname}_{volc}_ctl'\n", " print(key)\n", " tspan = slice(mm-1, mm-1+n_years*12)\n", " da = xr.open_dataarray(ifile).isel(time=tspan).mean('time')\n", " das[key] = da\n", "\n", " # # ctl_nudge\n", " # ifile = f'data/nudgeclimo_all_model_CTL1860_tigercpu_intelmpi_18_576PE.atmos_month.{dataname}.nc'\n", " # for volc, yyyy, mm in zip(volcs, years, months):\n", " # key = f'{volc}_nudge_ctl_{dataname}'\n", " # print(key)\n", " # tspan = slice(mm-1+12, mm-1+12+n_years*12)\n", " # da = xr.open_dataarray(ifile).isel(time=tspan).mean('time')\n", " # das[key] = da" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-10-09T15:14:38.003219Z", "start_time": "2018-10-09T15:13:55.745404Z" }, "code_folding": [ 0 ] }, "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. 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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" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "array(-0.354224)\n", "\n", "array(-0.213139)\n", "\n", "array(-0.195988)\n", "\n", "array(-2.820136e-07)\n", "\n", "array(-1.794571e-07)\n", "\n", "array(-1.249531e-07)\n" ] } ], "source": [ "# plot revision_1 significance\n", "fig, axes = plt.subplots(3, 2, figsize=(9, 7), sharey=True)\n", "dataname = 't_surf'\n", "units = 'K'\n", "levels = np.arange(-1, 1.1, .2)\n", "\n", "ax = axes[0, 0]\n", "plt.sca(ax)\n", "volc = 'Pinatubo'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "# n_ens = da.en.size\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(a) $T_s$', loc='left')\n", "\n", "ax = axes[1, 0]\n", "plt.sca(ax)\n", "volc = 'Agung'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(b)', loc='left')\n", "\n", "ax = axes[2, 0]\n", "plt.sca(ax)\n", "volc = 'StMaria'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(c)', loc='left')\n", "\n", "\n", "dataname = 'precip'\n", "scale = 24*3600\n", "units = 'mm day$^{-1}$'\n", "levels = np.arange(-.5, .51, .1)\n", "\n", "ax = axes[0, 1]\n", "plt.sca(ax)\n", "volc = 'Pinatubo'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(d) Prcp', loc='left')\n", "\n", "ax = axes[1, 1]\n", "plt.sca(ax)\n", "volc = 'Agung'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(e) ', loc='left')\n", "\n", "ax = axes[2, 1]\n", "plt.sca(ax)\n", "volc = 'StMaria'\n", "\n", "keyv, keyc = f'{dataname}_{volc}', f'{dataname}_{volc}_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "spread = p2t(0.05, df=n_ens-1) * da.std('en') * n_ens**(-1/2)\n", "L = np.abs( da.mean('en') ) > spread\n", "da.mean('en').where(L).pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(f)', loc='left')\n", "\n", "plt.tight_layout()\n", "\n", "figname = f'figs/fig_maps_TP_sig.pdf'\n", "plt.savefig(figname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## fig: 18 months since eruption" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-10-09T15:58:19.071275Z", "start_time": "2018-10-09T15:58:05.255869Z" }, "code_folding": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t_surf_Pinatubo_18months\n", "t_surf_Agung_18months\n", "t_surf_StMaria_18months\n", "t_surf_Pinatubo_18months_ctl\n" ] }, { "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "t_surf_Agung_18months_ctl\n", "t_surf_StMaria_18months_ctl\n", "precip_Pinatubo_18months\n", "precip_Agung_18months\n", "precip_StMaria_18months\n", "precip_Pinatubo_18months_ctl\n", "precip_Agung_18months_ctl\n", "precip_StMaria_18months_ctl\n" ] } ], "source": [ "# data\n", "datanames = ['t_surf', 'precip']\n", "volcs = ['Pinatubo', 'Agung', 'StMaria']\n", "years = [1902, 1963, 1991]\n", "months = [10, 3, 6]\n", "n_months = 18\n", "\n", "for dataname in datanames:\n", " # volc\n", " ifiles = [f'data/{volc}_PI_ens_noleap.atmos_month.{dataname}.nc'\n", " for volc in volcs]\n", " for volc, ifile, yyyy, mm in zip(volcs, ifiles, years, months):\n", " key = f'{dataname}_{volc}_{n_months}months'\n", " print(key)\n", " tspan = slice(mm-1, mm-1+ n_months)\n", " da = xr.open_dataarray(ifile).isel(time=tspan).mean('time')\n", " das[key] = da\n", "\n", " # # volc_nudge\n", " # ifiles = [f'data/{volc}_ens_noleap_nudgeclimo_all_model1860.atmos_month.{dataname}.nc'\n", " # for volc in volcs]\n", " # for volc, ifile, yyyy, mm in zip(volcs, ifiles, years, months):\n", " # key = f'{volc}_nudge_{dataname}'\n", " # print(key)\n", " # tspan = slice(f'{yyyy+1}-{mm:02d}', f'{yyyy+1+n_years}-{mm-1:02d}')\n", " # da = xr.open_dataarray(ifile).sel(time=tspan).mean('time')\n", " # das[key] = da\n", "\n", " # ctl\n", " ifile = f'data/CTL1860_noleap_tigercpu_intelmpi_18_576PE.atmos_month.{dataname}.nc'\n", " for volc, yyyy, mm in zip(volcs, years, months):\n", " key = f'{dataname}_{volc}_{n_months}months_ctl'\n", " print(key)\n", " tspan = slice(mm-1, mm-1+n_months)\n", " da = xr.open_dataarray(ifile).isel(time=tspan).mean('time')\n", " das[key] = da\n", "\n", " # # ctl_nudge\n", " # ifile = f'data/nudgeclimo_all_model_CTL1860_tigercpu_intelmpi_18_576PE.atmos_month.{dataname}.nc'\n", " # for volc, yyyy, mm in zip(volcs, years, months):\n", " # key = f'{volc}_nudge_ctl_{dataname}'\n", " # print(key)\n", " # tspan = slice(mm-1+12, mm-1+12+n_years*12)\n", " # da = xr.open_dataarray(ifile).isel(time=tspan).mean('time')\n", " # das[key] = da" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2018-10-09T15:59:05.733149Z", "start_time": "2018-10-09T15:59:02.960363Z" }, "code_folding": [], "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 = $('
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');\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 = $('
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this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "array(-0.3937417800674684)\n", "\n", "array(-0.2532316121322928)\n", "\n", "array(-0.20042861144798724)\n", "\n", "array(-3.8491131248282643e-07)\n", "\n", "array(-2.198022610025778e-07)\n", "\n", "array(-1.6032798386993648e-07)\n" ] } ], "source": [ "# plot\n", "fig, axes = plt.subplots(3, 2, figsize=(9, 7), sharey=True)\n", "dataname = 't_surf'\n", "units = 'K'\n", "levels = np.arange(-1, 1.1, .2)\n", "\n", "ax = axes[0, 0]\n", "plt.sca(ax)\n", "volc = 'Pinatubo'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "da.mean('en').rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(a) $T_s$', loc='left')\n", "\n", "ax = axes[1, 0]\n", "plt.sca(ax)\n", "volc = 'Agung'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "da.mean('en').rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(b)', loc='left')\n", "\n", "ax = axes[2, 0]\n", "plt.sca(ax)\n", "volc = 'StMaria'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "da.mean('en').rename(units).plot(robust=True, levels=levels, center=0, ax=ax, rasterized=True)\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel(f'{volc}')\n", "ax.set_title(f'(c)', loc='left')\n", "\n", "\n", "dataname = 'precip'\n", "scale = 24*3600\n", "units = 'mm day$^{-1}$'\n", "levels = np.arange(-.5, .51, .1)\n", "\n", "ax = axes[0, 1]\n", "plt.sca(ax)\n", "volc = 'Pinatubo'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "da.mean('en').pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(d) Prcp', loc='left')\n", "\n", "ax = axes[1, 1]\n", "plt.sca(ax)\n", "volc = 'Agung'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "da.mean('en').pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(e) ', loc='left')\n", "\n", "ax = axes[2, 1]\n", "plt.sca(ax)\n", "volc = 'StMaria'\n", "\n", "keyv, keyc = f'{dataname}_{volc}_{n_months0}-{n_months1}mon', f'{dataname}_{volc}_{n_months0}-{n_months1}mon_ctl'\n", "da = das[keyv] - das[keyc]\n", "# da.mean('en').pipe(lambda da: da/da.geo.fldmean()*(-1)) \\\n", "# .rename('K K$^{-1}$').plot(robust=True, levels=levels, center=0, ax=ax)\n", "da.mean('en').pipe(lambda x: x*scale).rename(units).plot(robust=True, \n", " levels=levels, \n", " center=0, \n", " ax=ax,\n", " cmap='BrBG',\n", " rasterized=True\n", " )\n", "mapplot(ax=ax)\n", "\n", "print(da.mean('en').geo.fldmean())\n", "ax.set_xlabel('')\n", "ax.set_ylabel('')\n", "ax.set_title(f'(f)', loc='left')\n", "\n", "plt.suptitle('18-36 months since eruption', x='.9', ha='right')\n", "\n", "plt.tight_layout()\n", "\n", "figname = f'figs/fig_maps_TP_{n_months0}-{n_months1}mon.pdf'\n", "plt.savefig(figname)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-10-09T16:01:53.421084Z", "start_time": "2018-10-09T16:01:53.417654Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "" ] } ], "metadata": { "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 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