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In [38]:

    
%pylab inline
import pandas as pd









    



Populating the interactive namespace from numpy and matplotlib



In [39]:

    
df = pd.read_csv("../data/ChungCheonDC/CompositeETCdata.csv")
df_DC = pd.read_csv("../data/ChungCheonDC/CompositeDCdata.csv")
df_DCstd = pd.read_csv("../data/ChungCheonDC/CompositeDCstddata.csv")



In [40]:

    
sys.path.append("../codes/")
from DCdata import readReservoirDC_all
directory = "../data/ChungCheonDC/"
dat_temp,height_temp, ID = readReservoirDC_all(directory+"20151231180000.apr")



In [41]:

    
# missininds = np.arange(df_DC[electrodeID[elecind]].values.size)[np.isnan(df_DC[electrodeID[elecind]].values)]
electrodeID = df_DC.keys()[1:-1]



In [42]:

    
out = hist(df_DC.median().values, bins=100)



In [43]:

    
df_DC.median().values?









    



Object `values` not found.



In [ ]:

    
df_DC.median().values



In [44]:

    
df_DC.median().values









    Out[44]:





array([ 182.      ,   54.957775,   60.77825 ,   61.893725,   81.502875,
         74.236625,   71.154   ,   76.283925,   64.5145  ,   46.413975,
         80.9049  ,   80.857025,   79.03195 ,   72.678725,   77.60575 ,
         77.540725,   66.782325,   90.69085 ,   56.670475,  114.65225 ,
         48.745225,  133.24525 ,   55.8378  ,   78.876225,   73.341725,
         64.929425,   63.9662  ,   62.580075,   81.69775 ,   61.57185 ,
         71.266175,   73.75785 ,   76.4625  ,   67.358425,   77.53645 ,
         70.0028  ,   65.586575,   64.085925,   77.13475 ,   67.171325,
         70.92615 ,   64.996075,   75.73305 ,   60.1916  ,   83.89205 ,
         73.1933  ,   68.165425,   67.8629  ,   69.58525 ,   64.1825  ,
         69.552275,   68.28275 ,   71.82245 ,   73.485   ,   65.445675,
         81.83345 ,   76.00425 ,   77.50465 ,   68.705675,   55.037875,
         53.798525,   83.004925,   88.616475,   78.814725,   72.0939  ,
         83.078475,   71.1599  ,   84.628825,   70.7179  ,   87.08305 ,
         74.60845 ,   84.326325,   90.68615 ,   56.76075 ,   86.6665  ,
         76.4293  ,   65.9395  ,   70.51525 ,   78.550825,   76.270025,
         66.82355 ,   75.853475,   78.27375 ,   75.423675,   69.08645 ,
         70.932775,   72.082625,   67.34475 ,   72.9478  ,   79.2426  ,
         75.1249  ,   69.707575,   73.501   ,   69.9865  ,   71.0091  ,
         79.696875,   70.368225,   65.01115 ,   70.217425,   61.2535  ,
         58.83135 ,   55.217875,   81.0828  ,   78.8378  ,   63.1637  ,
         81.86435 ,   85.09115 ,   70.175725,   62.5037  ,   68.1218  ,
         56.48955 ,   95.18085 ,   90.217275,   79.915525,   83.662575,
         77.854325,   91.9361  ,   70.64935 ,  110.204   ,   64.98055 ,
        124.0655  ,   70.520925,   95.3764  ,   66.85515 ,   95.791075,
         80.362275,   74.0566  ,   92.421725,   76.584075,   84.362125,
         74.138825,   87.971575,   82.5368  ,   81.135325,   72.257675,
         78.55055 ,   72.563725,   78.9858  ,   78.876925,   86.6743  ,
         72.422825,   78.5119  ,   68.61925 ,   83.00215 ,   78.247575,
         85.8018  ,   65.9131  ,   64.66975 ,   66.0319  ,   64.52605 ,
         51.119975,  100.34375 ,   88.381325,   67.085075,   92.3171  ,
         75.111725,   67.988   ,   79.628525,   75.2512  ,   64.4584  ,
         98.77405 ,   91.287925,   92.4816  ,   79.963575,  102.4875  ,
         77.959875,  112.24275 ,   84.587375,  108.693   ,  106.99825 ,
         75.857675,  110.1365  ,   76.236125,  102.1945  ,   89.3078  ,
         97.0182  ,   91.461825,   85.457275,   93.705375,   88.4982  ,
         97.56905 ,   91.987275,   87.8912  ,   82.391875,   81.284475,
         87.906775,   85.578875,   90.148975,   84.333375,   83.112875,
         73.690925,   82.26985 ,   90.471275,   77.630025,   74.956225,
         69.458275,   68.107225,   65.027175,   58.958125,  122.53575 ,
         99.822775,   78.858875,   80.526325,   73.1258  ,   85.7325  ,
         86.651975,   85.3754  ,   64.00085 ,   99.4715  ,  102.24575 ,
         86.36745 ,  104.7895  ,   87.234175,  122.69925 ,   85.25915 ,
        137.70225 ,   94.634775,  111.94375 ,   86.312275,  122.70175 ,
         77.85195 ,  110.669   ,  114.19825 ,   95.477625,  100.83625 ,
         92.182225,  109.5595  ,   97.781675,  108.56675 ,  101.059   ,
        100.06625 ,   85.50985 ,   98.0885  ,   95.349775,   98.40435 ,
         88.735125,   97.143425,   77.15185 ,   86.765825,   89.2291  ,
         86.153075,   74.10385 ,   81.162175,   66.784925,   64.600375,
         67.272275,  136.15075 ,  118.27425 ,   67.304775,   77.798525,
         88.9863  ,   90.43905 ,   95.75385 ,   85.111025,   62.98085 ,
        107.5435  ,   93.078   ,  111.17325 ,   85.6652  ,  133.72025 ,
         91.4524  ,  135.28775 ,  117.3005  ,   96.721025,  122.156   ,
         93.832725,  120.39375 ,   82.1488  ,  138.5535  ,  109.318   ,
        102.94125 ,  105.61275 ,  104.69725 ,  118.52375 ,  104.90025 ,
        117.05675 ,  108.694   ,   99.973525,   99.3824  ,  104.1945  ,
        108.77825 ,   96.298825,  100.802   ,   89.605125,   88.933525,
         89.7252  ,   87.0876  ,   85.376225,   75.9064  ,   75.59105 ,
         64.0734  ,   72.851825,  158.2175  ,   99.5084  ,   65.1621  ,
         98.522775,   91.150225,   97.550975,   92.30015 ,   80.0766  ,
         69.076375,   94.1428  ,  115.25475 ,   89.46    ,  125.539   ,
         95.1621  ,  140.82625 ,  112.294   ,  116.381   ,  103.3265  ,
        130.54625 ,   89.315525,  124.086   ,  100.17125 ,  129.72725 ,
        115.2705  ,  105.81025 ,  120.0425  ,  109.66375 ,  125.3765  ,
        111.825   ,  125.818   ,  108.27625 ,  113.054   ,  103.837   ,
        115.56575 ,  102.7265  ,  107.415   ,   92.634625,   96.543575,
         92.874125,   87.314525,   80.320025,   82.728725,   71.001975,
         73.3091  ,   71.244225,  127.56825 ,   97.238525,   73.651525,
         95.7646  ,   98.001675,   91.864725,   85.219975,   84.619975,
         59.797075,  113.69975 ,   91.164625,  126.93775 ,   93.675425,
        144.287   ,  115.11875 ,  111.2815  ,  122.13375 ,  108.4025  ,
        120.839   ,   92.0225  ,  148.412   ,   93.01125 ,  136.3615  ,
        117.8445  ,  117.74825 ,  123.375   ,  114.975   ,  131.35125 ,
        116.62025 ,  121.66175 ,  119.1735  ,  115.92725 ,  113.22575 ,
        107.662   ,  111.3505  ,   95.046725,   99.63305 ,  104.20075 ,
         85.599425,   79.901875,   77.246625,   78.541575,   62.370625,
         84.020025])



In [45]:

    
df_DC.median().values









    Out[45]:





array([ 182.      ,   54.957775,   60.77825 ,   61.893725,   81.502875,
         74.236625,   71.154   ,   76.283925,   64.5145  ,   46.413975,
         80.9049  ,   80.857025,   79.03195 ,   72.678725,   77.60575 ,
         77.540725,   66.782325,   90.69085 ,   56.670475,  114.65225 ,
         48.745225,  133.24525 ,   55.8378  ,   78.876225,   73.341725,
         64.929425,   63.9662  ,   62.580075,   81.69775 ,   61.57185 ,
         71.266175,   73.75785 ,   76.4625  ,   67.358425,   77.53645 ,
         70.0028  ,   65.586575,   64.085925,   77.13475 ,   67.171325,
         70.92615 ,   64.996075,   75.73305 ,   60.1916  ,   83.89205 ,
         73.1933  ,   68.165425,   67.8629  ,   69.58525 ,   64.1825  ,
         69.552275,   68.28275 ,   71.82245 ,   73.485   ,   65.445675,
         81.83345 ,   76.00425 ,   77.50465 ,   68.705675,   55.037875,
         53.798525,   83.004925,   88.616475,   78.814725,   72.0939  ,
         83.078475,   71.1599  ,   84.628825,   70.7179  ,   87.08305 ,
         74.60845 ,   84.326325,   90.68615 ,   56.76075 ,   86.6665  ,
         76.4293  ,   65.9395  ,   70.51525 ,   78.550825,   76.270025,
         66.82355 ,   75.853475,   78.27375 ,   75.423675,   69.08645 ,
         70.932775,   72.082625,   67.34475 ,   72.9478  ,   79.2426  ,
         75.1249  ,   69.707575,   73.501   ,   69.9865  ,   71.0091  ,
         79.696875,   70.368225,   65.01115 ,   70.217425,   61.2535  ,
         58.83135 ,   55.217875,   81.0828  ,   78.8378  ,   63.1637  ,
         81.86435 ,   85.09115 ,   70.175725,   62.5037  ,   68.1218  ,
         56.48955 ,   95.18085 ,   90.217275,   79.915525,   83.662575,
         77.854325,   91.9361  ,   70.64935 ,  110.204   ,   64.98055 ,
        124.0655  ,   70.520925,   95.3764  ,   66.85515 ,   95.791075,
         80.362275,   74.0566  ,   92.421725,   76.584075,   84.362125,
         74.138825,   87.971575,   82.5368  ,   81.135325,   72.257675,
         78.55055 ,   72.563725,   78.9858  ,   78.876925,   86.6743  ,
         72.422825,   78.5119  ,   68.61925 ,   83.00215 ,   78.247575,
         85.8018  ,   65.9131  ,   64.66975 ,   66.0319  ,   64.52605 ,
         51.119975,  100.34375 ,   88.381325,   67.085075,   92.3171  ,
         75.111725,   67.988   ,   79.628525,   75.2512  ,   64.4584  ,
         98.77405 ,   91.287925,   92.4816  ,   79.963575,  102.4875  ,
         77.959875,  112.24275 ,   84.587375,  108.693   ,  106.99825 ,
         75.857675,  110.1365  ,   76.236125,  102.1945  ,   89.3078  ,
         97.0182  ,   91.461825,   85.457275,   93.705375,   88.4982  ,
         97.56905 ,   91.987275,   87.8912  ,   82.391875,   81.284475,
         87.906775,   85.578875,   90.148975,   84.333375,   83.112875,
         73.690925,   82.26985 ,   90.471275,   77.630025,   74.956225,
         69.458275,   68.107225,   65.027175,   58.958125,  122.53575 ,
         99.822775,   78.858875,   80.526325,   73.1258  ,   85.7325  ,
         86.651975,   85.3754  ,   64.00085 ,   99.4715  ,  102.24575 ,
         86.36745 ,  104.7895  ,   87.234175,  122.69925 ,   85.25915 ,
        137.70225 ,   94.634775,  111.94375 ,   86.312275,  122.70175 ,
         77.85195 ,  110.669   ,  114.19825 ,   95.477625,  100.83625 ,
         92.182225,  109.5595  ,   97.781675,  108.56675 ,  101.059   ,
        100.06625 ,   85.50985 ,   98.0885  ,   95.349775,   98.40435 ,
         88.735125,   97.143425,   77.15185 ,   86.765825,   89.2291  ,
         86.153075,   74.10385 ,   81.162175,   66.784925,   64.600375,
         67.272275,  136.15075 ,  118.27425 ,   67.304775,   77.798525,
         88.9863  ,   90.43905 ,   95.75385 ,   85.111025,   62.98085 ,
        107.5435  ,   93.078   ,  111.17325 ,   85.6652  ,  133.72025 ,
         91.4524  ,  135.28775 ,  117.3005  ,   96.721025,  122.156   ,
         93.832725,  120.39375 ,   82.1488  ,  138.5535  ,  109.318   ,
        102.94125 ,  105.61275 ,  104.69725 ,  118.52375 ,  104.90025 ,
        117.05675 ,  108.694   ,   99.973525,   99.3824  ,  104.1945  ,
        108.77825 ,   96.298825,  100.802   ,   89.605125,   88.933525,
         89.7252  ,   87.0876  ,   85.376225,   75.9064  ,   75.59105 ,
         64.0734  ,   72.851825,  158.2175  ,   99.5084  ,   65.1621  ,
         98.522775,   91.150225,   97.550975,   92.30015 ,   80.0766  ,
         69.076375,   94.1428  ,  115.25475 ,   89.46    ,  125.539   ,
         95.1621  ,  140.82625 ,  112.294   ,  116.381   ,  103.3265  ,
        130.54625 ,   89.315525,  124.086   ,  100.17125 ,  129.72725 ,
        115.2705  ,  105.81025 ,  120.0425  ,  109.66375 ,  125.3765  ,
        111.825   ,  125.818   ,  108.27625 ,  113.054   ,  103.837   ,
        115.56575 ,  102.7265  ,  107.415   ,   92.634625,   96.543575,
         92.874125,   87.314525,   80.320025,   82.728725,   71.001975,
         73.3091  ,   71.244225,  127.56825 ,   97.238525,   73.651525,
         95.7646  ,   98.001675,   91.864725,   85.219975,   84.619975,
         59.797075,  113.69975 ,   91.164625,  126.93775 ,   93.675425,
        144.287   ,  115.11875 ,  111.2815  ,  122.13375 ,  108.4025  ,
        120.839   ,   92.0225  ,  148.412   ,   93.01125 ,  136.3615  ,
        117.8445  ,  117.74825 ,  123.375   ,  114.975   ,  131.35125 ,
        116.62025 ,  121.66175 ,  119.1735  ,  115.92725 ,  113.22575 ,
        107.662   ,  111.3505  ,   95.046725,   99.63305 ,  104.20075 ,
         85.599425,   79.901875,   77.246625,   78.541575,   62.370625,
         84.020025])



In [46]:

    
istrt_march = 59
iend_march = 89
istrt_april = iend_march + 1
iend_april = istrt_april + 30
istrt_may = iend_april 
iend_may = istrt_may + 31


# print df_DC['date'][istrt_april:iend_april]



In [47]:

    
print df_DC['date'][istrt_may:iend_may]









    



120    2015-05-01
121    2015-05-02
122    2015-05-03
123    2015-05-04
124    2015-05-05
125    2015-05-06
126    2015-05-07
127    2015-05-08
128    2015-05-09
129    2015-05-10
130    2015-05-11
131    2015-05-12
132    2015-05-13
133    2015-05-14
134    2015-05-15
135    2015-05-16
136    2015-05-17
137    2015-05-18
138    2015-05-19
139    2015-05-20
140    2015-05-21
141    2015-05-22
142    2015-05-23
143    2015-05-24
144    2015-05-25
145    2015-05-26
146    2015-05-27
147    2015-05-28
148    2015-05-29
149    2015-05-30
150    2015-05-31
Name: date, dtype: object



In [48]:

    
def getMedian(istart, iend, df_DC):
    return df_DC.ix[istart:iend].median().values



In [49]:

    
year_medDC = getMedian(0, 364, df_DC)
march_medDC = getMedian(istrt_march, iend_march, df_DC)
april_medDC = getMedian(istrt_april, iend_april, df_DC)
may_medDC = getMedian(istrt_may, iend_may, df_DC)
may1_medDC = getMedian(iend_may, iend_may, df_DC)
may2_medDC = getMedian(iend_may+1, iend_may+1, df_DC)
mayweek1_medDC = getMedian(iend_may-15, iend_may-7, df_DC)
mayweek2_medDC = getMedian(iend_may-7, iend_may, df_DC)



In [50]:

    
print df_DC['date'][iend_may-13]
print df_DC['date'][iend_may-7]
print df_DC['date'][iend_may-6]
print df_DC['date'][iend_may]



In [51]:

    
import matplotlib
matplotlib.rcParams["font.size"] = 14



In [52]:

    
!mkdir images









    



mkdir: images: File exists



In [57]:

    
fig, ax = plt.subplots(2,2, figsize=(15, 10))
out = ax[0,0].hist(may1_medDC, bins=100, color='b', alpha=0.3)
out = ax[0,0].hist(may2_medDC, bins=100, color='r', alpha=0.5)
ax[0,0].legend(("May 31", "Day(June 1)"))
out = ax[0,1].hist(mayweek1_medDC, bins=100, color='b', alpha=0.3)
out = ax[0,1].hist(mayweek2_medDC, bins=100, color='r', alpha=0.5)
ax[0,1].legend(("May 19-May 25", "Week(May 27-June 2)"))
out = ax[1,0].hist(may_medDC, bins=100, color='b', alpha=0.3)
out = ax[1,0].hist(april_medDC, bins=100, color='r', alpha=0.5)
ax[1,0].legend(("April", "Month(May)"))

out = ax[1,1].hist(may1_medDC, bins=100, color='b', alpha=0.3)
out = ax[1,1].hist(year_medDC, bins=100, color='r', alpha=0.3)
ax[1,1].legend(("May 31","Year(2015)" ))

for ax_temp in ax.flatten():
    ax_temp.set_xlim(10,200)
    ax_temp.set_ylim(0,25)
    ax_temp.set_xscale("linear")
    ax_temp.set_xlabel("Apparent resistivity (ohm-m)")    
    ax_temp.set_ylabel("Frequency")    
    ax_temp.grid(True)
fig.savefig("images/Cheongcheonhistogram", dpi=200)



In [54]:

    
import matplotlib
matplotlib.rcParams["font.size"] = 12



In [55]:

    
ax1 = plt.subplot(111)
ax1_1 = ax1.twinx()
df.plot(figsize=(12,3), x='date', y='reservoirH', ax=ax1_1, color='k', linestyle='-', lw=2)
df.plot(figsize=(12,3), x='date', y='upperH_med', ax=ax1_1, color='b', linestyle='-', lw=2)
df.plot(figsize=(12,3), x='date', y='Temp (degree)', ax=ax1, color='r', linestyle='-', lw=2)
ax1.legend(loc=3, bbox_to_anchor=(1.05, 0.7))
ax1_1.legend(loc=3, bbox_to_anchor=(1.05, 0.4))
# itime_ref0 = 255
# itime_ref1 = 115
# ax1.plot(np.r_[itime_ref0, itime_ref0], np.r_[-5, 35], 'k-')
# ax1.plot(np.r_[itime_ref1, itime_ref1], np.r_[-5, 35], 'k-')
# print df['date'].values[itime_ref0]
ax1.grid(True)



In [ ]: