In [1]:

import salishsea_tools.river_202108 as rivers
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

In [2]:

rivers_puget = rivers.prop_dict['puget']
rivers_puget.keys()

Out[2]:

dict_keys(['Johnson', 'Jimmycomelately', 'SalmonSnow', 'Chimacum', 'Thorndike', 'Torboo', 'LittleBigQuilcene', 'Dosewalips', 'Duckabush', 'Fulton', 'Waketick', 'HammaHamma', 'Jorsted', 'Eagle', 'Lilliwaup', 'Finch', 'Skokomish', 'Rendsland', 'Tahuya', 'Mission', 'Union', 'Coulter', 'Minter', 'Burley', 'Olalla', 'Blackjack', 'ClearBarker', 'BigValley', 'BigBear', 'Swaback', 'Stavis', 'Anderson', 'Dewatta', 'Sherwood', 'DeerJohnsGoldboroughMill', 'Skookum', 'KennedySchneider', 'PerryMcClane', 'Deschutes', 'Woodward', 'Woodland', 'Chambers', 'NisquallyMcAllister', 'Puyallup', 'Hylebas', 'Duwamish1', 'Duwamish2', 'CedarSammamish'])

In [3]:

data = pd.read_csv('/ocean/cstang/MOAD/rivers_data/river_dailies_to_ts_rivers_20171210_20221231.csv')

In [4]:

data.iloc[:,3:]

Out[4]:

	Johnson [kg/m2/s]	Jimmycomelately [kg/m2/s]	SalmonSnow [kg/m2/s]	Chimacum [kg/m2/s]	Thorndike [kg/m2/s]	Torboo [kg/m2/s]	LittleBigQuilcene [kg/m2/s]	Dosewalips [kg/m2/s]	Duckabush [kg/m2/s]	Fulton [kg/m2/s]	...	Deschutes [kg/m2/s]	Woodward [kg/m2/s]	Woodland [kg/m2/s]	Chambers [kg/m2/s]	NisquallyMcAllister [kg/m2/s]	Puyallup [kg/m2/s]	Hylebas [kg/m2/s]	Duwamish1 [kg/m2/s]	Duwamish2 [kg/m2/s]	CedarSammamish [kg/m2/s]
0	0.014361	0.014347	0.071620	0.057317	0.014253	0.014272	0.099892	0.056994	0.039894	0.005701	...	0.059857	0.008521	0.008524	0.056353	0.424030	0.559204	0.002810	0.141188	0.141189	0.283189
1	0.014065	0.014052	0.070146	0.056137	0.013960	0.013979	0.097836	0.055820	0.039073	0.005584	...	0.058625	0.008345	0.008348	0.055193	0.415300	0.547691	0.002752	0.138281	0.138282	0.277359
2	0.013733	0.013720	0.068488	0.054810	0.013630	0.013648	0.095523	0.054501	0.038149	0.005452	...	0.057239	0.008148	0.008151	0.053889	0.405483	0.534745	0.002687	0.135013	0.135013	0.270803
3	0.013474	0.013462	0.067199	0.053778	0.013373	0.013391	0.093725	0.053475	0.037431	0.005349	...	0.056162	0.007995	0.007997	0.052874	0.397852	0.524681	0.002636	0.132472	0.132472	0.265706
4	0.013252	0.013240	0.066093	0.052894	0.013153	0.013171	0.092184	0.052596	0.036815	0.005261	...	0.055238	0.007863	0.007866	0.052005	0.391308	0.516050	0.002593	0.130293	0.130293	0.261335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1843	0.037919	0.037884	0.189112	0.151344	0.037635	0.037686	0.263764	0.150491	0.105339	0.015054	...	0.158051	0.022499	0.022507	0.148800	1.119644	1.476569	0.007419	0.372805	0.372806	0.747756
1844	0.036420	0.036386	0.181635	0.145361	0.036147	0.036196	0.253337	0.144542	0.101175	0.014459	...	0.151803	0.021609	0.021617	0.142918	1.075381	1.418195	0.007126	0.358067	0.358068	0.718194
1845	0.028637	0.028611	0.142821	0.114299	0.028423	0.028461	0.199201	0.113654	0.079555	0.011369	...	0.119364	0.016992	0.016997	0.112377	0.845580	1.115138	0.005603	0.281551	0.281552	0.564722
1846	0.026237	0.026213	0.130852	0.104719	0.026041	0.026076	0.182506	0.104129	0.072887	0.010416	...	0.109360	0.015568	0.015573	0.102959	0.774713	1.021679	0.005133	0.257954	0.257955	0.517393
1847	0.025001	0.024978	0.124684	0.099784	0.024813	0.024847	0.173904	0.099221	0.069452	0.009925	...	0.104205	0.014834	0.014839	0.098106	0.738199	0.973525	0.004891	0.245796	0.245797	0.493007

1848 rows × 48 columns

In [13]:

puget_rivers_avg = np.sum(data.iloc[:,3:],axis=1)

plt.plot(puget_rivers_avg)

Out[13]:

[<matplotlib.lines.Line2D at 0x7f135eb5a1a0>]

In [14]:

puget_rivers_avg.index = data['date']

In [19]:

puget_rivers_avg

puget_rivers_avg.to_csv('puget_rivers_avg.csv',index_label='Date')