| commit | c473244fb597e37461ab9706fd45475202c00e3c | [log] [tgz] |
|---|---|---|
| author | Lei <33376433+LeiRui@users.noreply.github.com> | Fri Apr 18 20:47:08 2025 +0800 |
| committer | GitHub <noreply@github.com> | Fri Apr 18 20:47:08 2025 +0800 |
| tree | 4a45f185c92eb1edc8cac3dda0da312af592e517 | |
| parent | 19037dcbd4baa5d59e325c04032af88cd30bcb51 [diff] |
Polish README.md
mvn clean package -DskipTests -pl -distribution.A python usage example of performing online sampling based on minimal areal difference:
m=100000 sql_online="select EBUG(s1,'m'='{}','e'='3373465') from root.sg.d5".format(m) sql=sql_online ip = "127.0.0.1" port_ = "6667" username_ = "root" password_ = "root" fetchsize = 100000 session = Session(ip, port_, username_, password_, fetchsize) session.open(False) # query: result = session.execute_query_statement(sql) df = result.todf() print(df) t_online=df.iloc[:,0] v_online=df.iloc[:,1] # interactive visualization: fig = go.Figure() fig.add_trace(go.Scatter(x=t_online, y=v_online)) fig.update_layout( autosize=False, width=850, height=650, ) fig.show()
A python usage example of querying the first m rows of the precomputed time series $T_{pre}$:
m=100000 sql_pre="select pre_t,pre_v from root.sg.d6 limit {}".format(m) sql=sql_pre ip = "127.0.0.1" port_ = "6667" username_ = "root" password_ = "root" fetchsize = 100000 session = Session(ip, port_, username_, password_, fetchsize) session.open(False) # query: result = session.execute_query_statement(sql) df = result.todf() print(df) df = df.rename(columns={'root.sg.d6.pre_t': 't', 'root.sg.d6.pre_v': 'v'}) df = df.sort_values(by='t') t_pre=df['t'] v_pre=df['v'] # interactive visualization: fig = go.Figure() fig.add_trace(go.Scatter(x=t_pre, y=v_pre)) fig.update_layout( autosize=False, width=850, height=650, ) fig.show()