Example
<p>[TOC]</p>
<p>Meteva is a modular verification Python package, which integrates calculation algorithms of various commonly used verification indexes (such as me, RMSE, TS, etc.).Advanced users can organize data and call these algorithms to complete verification calculation and draw results, but it may be difficult for entry-level users (especially those who are not proficient in Python) to organize data and draw results. In this case, Users can learn from this article as an introduction to meteva.</p>
<pre><code class="language-python">import meteva.base as meb # this module is for IO、data transformation and so on
import meteva.method as mem # this module contains verification algorithm
import meteva.product as mpd # this module contains veification tools
import numpy as np
import datetime
import copy
import pandas as pd</code></pre>
<p>The veification process with meteva can separated into two parts：</p>
<ul>
<li>part1: Data Collection</li>
<li>part2: Verification and Analysis </li>
</ul>
<h1>Data Collection</h1>
<pre><code class="language-python">################### below is the part of data collection code example
#set conserned time period
time_begin= datetime.datetime(2019,7,1,8,0)
time_end = datetime.datetime(2019,8,1,8,0)
#read station list
station = meb.read_stadata_from_micaps3(r"H:\Example_data\ob\temp_2m\BT19070102.000")
#set default value
station.iloc[:,-1] = meb.IV
##Collect observation
dir_ob = r"H:\Example_data\ob\temp_2m\BTYYMMDDHH.000" # OBS data path patten
sta_list = []
time0 = time_begin
time_end_ob = time_end + datetime.timedelta(hours = 72)
while time0 < time_end_ob:
path = meb.get_path(dir_ob,time0) # generate OBS data path from pattern and time
#read OBS data form Micaps3 format file
sta = meb.read_stadata_from_micaps3(path,station = station,time = time0,dtime = 0,level = 0,data_name = "OBS",show = True)
sta_list.append(sta) # add data to list
time0 += datetime.timedelta(hours = 3) # move to next time
ob_sta_all = meb.concat(sta_list) #contcat OBS data list into a DataFrame
#Collect ECMWF prediction
dir_ec = r"H:\Example_data\ecmwf\temp_2m\YYMMDD\BTYYMMDDHH.TTT" #ECMWF data path patten
sta_list =[]
time0 = time_begin
while time0 < time_end:
for dh in range(0,73,3):
path = meb.get_path(dir_ec,time0,dh) # generate ECMWF data path from pattern，time and valid time
#read ECMWF data form Micaps4 format file
grd = meb.read_griddata_from_micaps4(path,show = False)
if grd is not None:
sta = meb.interp_gs_linear(grd,station) #interpolate grid data to station
meb.set_stadata_coords(sta,time = time0,dtime = dh,level = 0) #add time,valid time and level informations to data
meb.set_stadata_names(sta,["ECMWF"])
sta_list.append(sta)
time0 += datetime.timedelta(hours = 12)
ec_sta_all = meb.concat(sta_list) #concat ECMWF data list int a DataFrame
#Collect GRAPES prediction
dir_grapes = r"H:\Example_data\grapes\temp_2m\YYMMDD\BTYYMMDDHH.TTT"
sta_list =[]
time0 = time_begin
while time0 < time_end:
for dh in range(0,73,3):
path = meb.get_path(dir_grapes,time0,dh) # generate GRAPES data path from pattern，time and valid time
#read GRAPES data form Micaps4 format file
grd = meb.read_griddata_from_micaps4(path,show = False)
if grd is not None:
sta = meb.interp_gs_linear(grd,station) #interpolate grid data to station
meb.set_stadata_coords(sta,time = time0,dtime = dh,level = 0) #add time,valid time and level informations to data
meb.set_stadata_names(sta,["GRAPES"])
sta_list.append(sta)
time0 += datetime.timedelta(hours = 12)
grapes_sta_all = meb.concat(sta_list) #Concat GRAPES data list into a DataFrame
#Combine obs and fst data into a DataFrame, based on the level,time, valid time and Id of station.
sta_all = meb.combine_on_obTime_id(ob_sta_all,[ec_sta_all,grapes_sta_all],need_match_ob=True)
sta_all.to_hdf(r"H:\Example_data\sta_all.h5","df") # output data-collection into file
###################below is the part of data collection code example</code></pre>
<pre><code>success read from H:\Example_data\ob\temp_2m\BT19070108.000
success read from H:\Example_data\ob\temp_2m\BT19070111.000
success read from H:\Example_data\ob\temp_2m\BT19070114.000
success read from H:\Example_data\ob\temp_2m\BT19070117.000
... ...
success read from H:\Example_data\ob\temp_2m\BT19080405.000</code></pre>
<h1>Verification and Analysis</h1>
<pre><code class="language-python"># load data-collection from file
sta_all = pd.read_hdf(r"H:\Example_data\sta_all.h5")
print(sta_all)</code></pre>
<pre><code> level time dtime id lon lat OBS ECMWF \
0 0 2019-07-01 08:00:00 0 54398 116.6 40.1 25.8 24.724
1 0 2019-07-01 08:00:00 0 54410 116.1 40.6 18.9 20.284
2 0 2019-07-01 08:00:00 0 54416 116.9 40.4 25.1 22.864
3 0 2019-07-01 08:00:00 0 54419 116.6 40.4 27.5 21.796
4 0 2019-07-01 08:00:00 0 54499 116.2 40.2 27.5 23.076
... ... ... ... ... ... ... ... ...
9187 0 2019-07-31 20:00:00 72 54410 116.1 40.6 18.8 21.556
9188 0 2019-07-31 20:00:00 72 54416 116.9 40.4 26.7 24.960
9189 0 2019-07-31 20:00:00 72 54419 116.6 40.4 27.0 22.920
9190 0 2019-07-31 20:00:00 72 54499 116.2 40.2 29.3 23.056
9191 0 2019-07-31 20:00:00 72 54412 116.6 40.7 25.4 21.640
GRAPES
0 23.844
1 21.740
2 22.704
3 23.136
4 23.736
... ...
9187 19.968
9188 23.404
9189 23.056
9190 22.568
9191 21.232
[9192 rows x 9 columns]</code></pre>
<h2>Example1</h2>
<p>the below line of code is used for caculation the mean absolute error group by valid time and plotting the result,<br />
where:</p>
<ul>
<li>mem.mae is the function name of mean absolute error in meteva</li>
<li>g="dtime" means group by valid time</li>
<li>plot = "bar" means plot with format of bar </li>
</ul>
<pre><code class="language-python">result = mpd.score(sta_all,mem.mae,g= "dtime",plot = "bar") </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/b995f01fcc78ac497767a6add5bd96a1" alt="" /></p>
<h2>Example2</h2>
<p>the below line of code is used for caculation the root mean square error grouped by time and plotting the result,<br />
where:</p>
<ul>
<li>mem.rmse is the function name of root mean square error in meteva</li>
<li>g="time" means group by time</li>
<li>plot = "line" means plot with format of line </li>
</ul>
<pre><code class="language-python">result = mpd.score(sta_all,mem.rmse,g= "time",plot = "line") </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/7445afd0349dd23014e61234a118b10a" alt="" /></p>
<h2>Example3</h2>
<p>the below line of code is used for caculation the threat score grouped by hour of time and plotting the result,<br />
where:</p>
<ul>
<li>mem.ts is the function name of threat score in meteva</li>
<li>grade_list = [30,35,37,40], is set to verify for kinds of threshold at the same time </li>
<li>g="hour" means group by hour of time</li>
<li>plot = "bar" means plot with format of bar </li>
</ul>
<pre><code class="language-python">result = mpd.score(sta_all,mem.ts,grade_list = [30,35,37,40],g = "hour",plot = "bar") </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/e943e904aab381678eb58972b4c882a5" alt="" /></p>
<h2>Example4</h2>
<p>the below line of code is used for caculation mean errof group by time and valid time and plotting the result,<br />
where:</p>
<ul>
<li>mem.me is the function name of mean error in meteva</li>
<li>x_y="time_dtime" is used for setting the x-axis and y-axis</li>
</ul>
<pre><code class="language-python">result = mpd.score_tdt(sta_all,mem.me,x_y = "time_dtime") </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/9374750a7de98a1b582c5bccea8630a3" alt="" /></p>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/f2f8a7a6e31672a3af0117eadf3f18c8" alt="" /></p>
<h2>Example5</h2>
<p>the below line of code is used for statistics and showing the mean error distribution<br />
where:</p>
<ul>
<li>mem.me is the function name of mean error in meteva</li>
<li>subplot = "member" is set to contian results of different model in one figure </li>
<li>ncol is the column number of subplots</li>
<li>width is the width of fig.</li>
</ul>
<pre><code class="language-python">result = mpd.score_id(sta_all,mem.me,subplot = "member",ncol = 2,width = 10) </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/6844bfd3c873a6bf8f736b999163d950" alt="" /></p>
<h2>Example6</h2>
<p>the below line of code is used for plotting regress relation between obs. and fst.<br />
where:</p>
<ul>
<li>mem.scatter_regress is the function name regress map in meteva</li>
</ul>
<pre><code class="language-python">result = mpd.plot(sta_all,mem.scatter_regress) </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitFile?sign=3f1b0920ff4b953cbbf6445b567ea62b" alt="" /></p>
<h2>Example7</h2>
<p>the below line of code is used for plotting frequency_histogram of obs. and fst.<br />
where:</p>
<ul>
<li>mem.frequency_histogram is the function name in meteva</li>
<li>grade_list is the grade setting</li>
</ul>
<pre><code class="language-python">result = mpd.plot(sta_all,mem.frequency_histogram,grade_list = np.arange(10,40,5).tolist()) </code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitfile/sign/89ea336ed143fea98dae0cac7f42493f" alt="" /></p>
<p>Above codes are some expample of using meteva, only little part funtions and parameters is show above, please find more details in other pages. </p>