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Icperts stoch xmls #4

Merged
merged 12 commits into from
Sep 11, 2023
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<plot_spec>
<connection>
<host>mohawk</host>
<database>mv_rrfse_icpert_stoch_nmep</database>
<user>******</user>
<password>******</password>
<management_system>mariadb</management_system>
</connection>
<rscript>/usr/local/R/bin/Rscript</rscript>
<folders>
<r_tmpl>/opt/vxwww/tomcat/webapps/metviewer//R_tmpl</r_tmpl>
<r_work>/opt/vxwww/tomcat/webapps/metviewer//R_work</r_work>
<plots>/d2/www/dtcenter/met/metviewer_output//plots</plots>
<data>/d2/www/dtcenter/met/metviewer_output//data</data>
<scripts>/d2/www/dtcenter/met/metviewer_output//scripts</scripts>
</folders>
<plot>
<template>series_plot.R_tmpl</template>
<dep>
<dep1>
<fcst_var name="APCP_01">
<stat>NBR_FSS</stat>
</fcst_var>
</dep1>
<dep2/>
</dep>
<series1>
<field name="model">
<val>RRFSE_CONUS_ICperts_nostoch.rrfs_conuscompact_3km_mem01</val>
<val>RRFSE_CONUS_ICperts_nostoch.rrfs_conuscompact_3km_mem07</val>
</field>
<field name="interp_pnts">
<val>9</val>
<val>25</val>
<val>49</val>
</field>
</series1>
<series2/>
<plot_fix>
<field equalize="false" name="vx_mask">
<set name="vx_mask_0">
<val>CONUS</val>
</set>
</field>
<field equalize="false" name="obtype">
<set name="obtype_1">
<val>CCPA</val>
</set>
</field>
<field equalize="false" name="interp_mthd">
<set name="interp_mthd_2">
<val>NBRHD_SQUARE</val>
</set>
</field>
<field equalize="false" name="fcst_init_beg">
<set name="fcst_init_beg_3">
<val>2022-04-30 00:00:00</val>
<val>2022-05-01 00:00:00</val>
<val>2022-05-02 00:00:00</val>
<val>2022-05-03 00:00:00</val>
<val>2022-05-04 00:00:00</val>
<val>2022-05-05 00:00:00</val>
<val>2022-05-06 00:00:00</val>
<val>2022-05-07 00:00:00</val>
<val>2022-05-08 00:00:00</val>
<val>2022-05-09 00:00:00</val>
<val>2022-05-10 00:00:00</val>
<val>2022-05-11 00:00:00</val>
<val>2022-05-12 00:00:00</val>
</set>
</field>
</plot_fix>
<plot_cond/>
<indep equalize="false" name="fcst_lead">
<val label="1" plot_val="">10000</val>
<val label="2" plot_val="">20000</val>
<val label="3" plot_val="">30000</val>
<val label="4" plot_val="">40000</val>
<val label="5" plot_val="">50000</val>
<val label="6" plot_val="">60000</val>
<val label="7" plot_val="">70000</val>
<val label="8" plot_val="">80000</val>
<val label="9" plot_val="">90000</val>
<val label="10" plot_val="">100000</val>
<val label="11" plot_val="">110000</val>
<val label="12" plot_val="">120000</val>
<val label="13" plot_val="">130000</val>
<val label="14" plot_val="">140000</val>
<val label="15" plot_val="">150000</val>
<val label="16" plot_val="">160000</val>
<val label="17" plot_val="">170000</val>
<val label="18" plot_val="">180000</val>
<val label="19" plot_val="">190000</val>
<val label="20" plot_val="">200000</val>
<val label="21" plot_val="">210000</val>
<val label="22" plot_val="">220000</val>
<val label="23" plot_val="">230000</val>
<val label="24" plot_val="">240000</val>
<val label="25" plot_val="">250000</val>
<val label="26" plot_val="">260000</val>
<val label="27" plot_val="">270000</val>
<val label="28" plot_val="">280000</val>
<val label="29" plot_val="">290000</val>
<val label="30" plot_val="">300000</val>
<val label="31" plot_val="">310000</val>
<val label="32" plot_val="">320000</val>
<val label="33" plot_val="">330000</val>
<val label="34" plot_val="">340000</val>
<val label="35" plot_val="">350000</val>
<val label="36" plot_val="">360000</val>
</indep>
<agg_stat>
<agg_nbrcnt>true</agg_nbrcnt>
<boot_repl>25</boot_repl>
<boot_random_seed/>
<boot_ci>perc</boot_ci>
<eveq_dis>false</eveq_dis>
<cache_agg_stat>false</cache_agg_stat>
<circular_block_bootstrap>true</circular_block_bootstrap>
</agg_stat>
<plot_stat>median</plot_stat>
<tmpl>
<data_file>plot_rrfs_spring_1hAPCP_0_FSS_mems.data</data_file>
<plot_file>plot_rrfs_spring_1hAPCP_0_FSS_mems.png</plot_file>
<r_file>plot_rrfs_spring_1hAPCP_0_FSS_mems.R</r_file>
<title>FSS: 1h APCP &gt; 0 mm Random Members at 3x3, 5x5, 7x7</title>
<x_label>Forecast Lead Time (h)</x_label>
<y1_label>FSS</y1_label>
<y2_label/>
<caption/>
<job_title>rrfs_spring_1hAPCP_0_FSS_mems</job_title>
<keep_revisions>false</keep_revisions>
<listdiffseries1>list()</listdiffseries1>
<listdiffseries2>list()</listdiffseries2>
</tmpl>
<execution_type>Python</execution_type>
<event_equal>false</event_equal>
<vert_plot>false</vert_plot>
<x_reverse>false</x_reverse>
<num_stats>false</num_stats>
<indy1_stag>false</indy1_stag>
<indy2_stag>false</indy2_stag>
<start_from_zero>false</start_from_zero>
<grid_on>true</grid_on>
<sync_axes>false</sync_axes>
<dump_points1>false</dump_points1>
<dump_points2>false</dump_points2>
<log_y1>false</log_y1>
<log_y2>false</log_y2>
<varianceinflationfactor>false</varianceinflationfactor>
<plot_type>png16m</plot_type>
<plot_height>8.5</plot_height>
<plot_width>11</plot_width>
<plot_res>72</plot_res>
<plot_units>in</plot_units>
<mar>c(8,4,5,4)</mar>
<mgp>c(1,1,0)</mgp>
<cex>1</cex>
<title_weight>2</title_weight>
<title_size>1.4</title_size>
<title_offset>-2</title_offset>
<title_align>0.5</title_align>
<xtlab_orient>1</xtlab_orient>
<xtlab_perp>-0.75</xtlab_perp>
<xtlab_horiz>0.5</xtlab_horiz>
<xtlab_freq>0</xtlab_freq>
<xtlab_size>1</xtlab_size>
<xlab_weight>1</xlab_weight>
<xlab_size>1</xlab_size>
<xlab_offset>2</xlab_offset>
<xlab_align>0.5</xlab_align>
<ytlab_orient>1</ytlab_orient>
<ytlab_perp>0.5</ytlab_perp>
<ytlab_horiz>0.5</ytlab_horiz>
<ytlab_size>1</ytlab_size>
<ylab_weight>1</ylab_weight>
<ylab_size>1</ylab_size>
<ylab_offset>-2</ylab_offset>
<ylab_align>0.5</ylab_align>
<grid_lty>3</grid_lty>
<grid_col>#cccccc</grid_col>
<grid_lwd>1</grid_lwd>
<grid_x>listX</grid_x>
<x2tlab_orient>1</x2tlab_orient>
<x2tlab_perp>1</x2tlab_perp>
<x2tlab_horiz>0.5</x2tlab_horiz>
<x2tlab_size>0.8</x2tlab_size>
<x2lab_size>0.8</x2lab_size>
<x2lab_offset>-0.5</x2lab_offset>
<x2lab_align>0.5</x2lab_align>
<y2tlab_orient>1</y2tlab_orient>
<y2tlab_perp>0.5</y2tlab_perp>
<y2tlab_horiz>0.5</y2tlab_horiz>
<y2tlab_size>1</y2tlab_size>
<y2lab_size>1</y2lab_size>
<y2lab_offset>1</y2lab_offset>
<y2lab_align>0.5</y2lab_align>
<legend_box>o</legend_box>
<legend_inset>c(0, -.25)</legend_inset>
<legend_ncol>3</legend_ncol>
<legend_size>0.8</legend_size>
<caption_weight>1</caption_weight>
<caption_col>#333333</caption_col>
<caption_size>0.8</caption_size>
<caption_offset>3</caption_offset>
<caption_align>0</caption_align>
<ci_alpha>0.05</ci_alpha>
<eqbound_low>-0.001</eqbound_low>
<eqbound_high>0.001</eqbound_high>
<plot_ci>c("none","none","none","none","none","none")</plot_ci>
<show_signif>c(FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)</show_signif>
<plot_disp>c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE)</plot_disp>
<colors>c("#ff0000FF","#ff0000FF","#ff0000FF","#0000ffFF","#0000ffFF","#0000ffFF")</colors>
<pch>c(20,20,20,20,20,20)</pch>
<type>c("b","b","b","b","b","b")</type>
<lty>c(1,2,3,1,2,3)</lty>
<lwd>c(2,2,2,2,2,2)</lwd>
<con_series>c(1,1,1,1,1,1)</con_series>
<order_series>c(1,2,3,4,5,6)</order_series>
<plot_cmd/>
<legend>c("no stoch mem 1 (3x3)","no stoch mem 1 (5x5)","no stoch mem 1 (7x7)","no stoch mem 7 (3x3)","no stoch mem 7 (5x5)","no stoch mem 7 (7x7)")</legend>
<create_html>TRUE</create_html>
<y1_lim>c()</y1_lim>
<x1_lim>c()</x1_lim>
<y1_bufr>0.04</y1_bufr>
<y2_lim>c()</y2_lim>
</plot>
</plot_spec>
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