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| 1 | +<?xml version="1.0" encoding="UTF-8"?> |
| 2 | +<Export generator="IRIS" version="26"> |
| 3 | +<Class name="INFORMATION.SHOWCASE004"> |
| 4 | +<Description> |
| 5 | +</Description> |
| 6 | +<Super>Ens.BusinessProcessBPL</Super> |
| 7 | +<TimeCreated>64998,2501.963342</TimeCreated> |
| 8 | + |
| 9 | +<Storage name="Default"> |
| 10 | +<Type>%Storage.Persistent</Type> |
| 11 | +</Storage> |
| 12 | + |
| 13 | +<XData name="BPL"> |
| 14 | +<Description> |
| 15 | +BPL Definition</Description> |
| 16 | +<XMLNamespace>http://www.intersystems.com/bpl</XMLNamespace> |
| 17 | +<Data><![CDATA[ |
| 18 | +<process language='objectscript' request='Ens.Request' response='Ens.Response' component='1' height='2000' width='2000' > |
| 19 | +<sequence xend='200' yend='450' > |
| 20 | +<code name='Correlation Matrix: Tabular' xpos='200' ypos='250' > |
| 21 | +<annotation><![CDATA[import pyodbc |
| 22 | +import pandas as pd |
| 23 | +cnxn=pyodbc.connect(('DSN=IRIS(USER);UID=_SYSTEM;PWD=password'),autocommit=True) |
| 24 | +Data=pd.read_sql('SELECT * FROM SQLUser.CANNIBALIZATION_VOLUME_YEAR_WEEK_CATEGORY_DESC',cnxn) |
| 25 | +Data0=Data.drop(['WEEK'],axis=1) |
| 26 | +Data0['BRATWURST']=pd.to_numeric(Data0['BRATWURST']) |
| 27 | +Data0['CERVELAS']=pd.to_numeric(Data0['CERVELAS']) |
| 28 | +Data0['CHARCUTERIE']=pd.to_numeric(Data0['CHARCUTERIE']) |
| 29 | +Data0['DAUERFLEISCHWAREN']=pd.to_numeric(Data0['DAUERFLEISCHWAREN']) |
| 30 | +Data0['GEFLUEGEL']=pd.to_numeric(Data0['GEFLUEGEL']) |
| 31 | +Data0['GERAEUCHERTES_ZUM_KOCHEN']=pd.to_numeric(Data0['GERAEUCHERTES_ZUM_KOCHEN']) |
| 32 | +Data0['HACKFLEISCH']=pd.to_numeric(Data0['HACKFLEISCH']) |
| 33 | +Data0['INNEREIEN_DIVERSES']=pd.to_numeric(Data0['INNEREIEN_DIVERSES']) |
| 34 | +Data0['KALB']=pd.to_numeric(Data0['KALB']) |
| 35 | +Data0['KANINCHEN']=pd.to_numeric(Data0['KANINCHEN']) |
| 36 | +Data0['LAMM']=pd.to_numeric(Data0['LAMM']) |
| 37 | +Data0['MARINADEN']=pd.to_numeric(Data0['MARINADEN']) |
| 38 | +Data0['PASTETEN_TERRINEN_STREICHWURST']=pd.to_numeric(Data0['PASTETEN_TERRINEN_STREICHWURST']) |
| 39 | +Data0['PFANNENFERTIGES']=pd.to_numeric(Data0['PFANNENFERTIGES']) |
| 40 | +Data0['PFERD']=pd.to_numeric(Data0['PFERD']) |
| 41 | +Data0['RIND']=pd.to_numeric(Data0['RIND']) |
| 42 | +Data0['SALAMI_ROHWURST_AM_STUECK']=pd.to_numeric(Data0['SALAMI_ROHWURST_AM_STUECK']) |
| 43 | +Data0['SCHINKEN']=pd.to_numeric(Data0['SCHINKEN']) |
| 44 | +Data0['SCHWEIN']=pd.to_numeric(Data0['SCHWEIN']) |
| 45 | +Data0['UEBRIGE_BRUEHWURST_STUECK']=pd.to_numeric(Data0['UEBRIGE_BRUEHWURST_STUECK']) |
| 46 | +Data0['WIENERLI_KNACKERLI_FRANKFURTERLI']=pd.to_numeric(Data0['WIENERLI_KNACKERLI_FRANKFURTERLI']) |
| 47 | +corrmat=Data0.corr() |
| 48 | +corrmat.columns.name='CATEGORY' |
| 49 | +//PyRun_SimpleString("import sys |
| 50 | +//PyRun_SimpleString("orig_stdout=sys.stdout |
| 51 | +//PyRun_SimpleString("f=open('C:/IRIS+Python/IRIS+Python_output_model_001.txt','w') |
| 52 | +//PyRun_SimpleString("sys.stdout=f |
| 53 | +print(corrmat.to_string()) |
| 54 | +//PyRun_SimpleString("sys.stdout=orig_stdout |
| 55 | +//PyRun_SimpleString("f.close() |
| 56 | +cnxn.close()]]]]><![CDATA[></annotation> |
| 57 | +<![CDATA[ Do ##class(CONVERGENCE.SHOWCASE004USE01).Go()]]]]><![CDATA[> |
| 58 | +</code> |
| 59 | +<code name='Correlation Matrix: Graph' xpos='200' ypos='350' > |
| 60 | +<annotation><![CDATA[import matplotlib |
| 61 | +import matplotlib.pyplot as plt |
| 62 | +import seaborn as sns |
| 63 | +f=plt.figure() |
| 64 | +sns.heatmap(corrmat,xticklabels=corrmat.columns,yticklabels=corrmat.columns) |
| 65 | +plt.title('Correlation analysis of category sales volumes (by year/month)') |
| 66 | +f.savefig('C:/IRIS+Python/SHOWCASE004USE02.png') |
| 67 | +plt.close(f)]]]]><![CDATA[></annotation> |
| 68 | +<![CDATA[ Do ##class(CONVERGENCE.SHOWCASE004USE02).Go()]]]]><![CDATA[> |
| 69 | +</code> |
| 70 | +</sequence> |
| 71 | +</process> |
| 72 | +]]></Data> |
| 73 | +</XData> |
| 74 | +</Class> |
| 75 | +</Export> |
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