In [1]:

    

# Render our plots inline
%matplotlib inline
%pylab inline  
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab


    

    




Populating the interactive namespace from numpy and matplotlib

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# General Plotting Parameters
mpl.rcParams['figure.figsize'] = (8,5)
mpl.rcParams['lines.linewidth'] = 2.5
mpl.rcParams['font.weight'] = 'bold'
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['font.size'] = 14.
mpl.rcParams['legend.fontsize'] = 12.
mpl.rcParams['axes.labelsize'] = 12.
mpl.rcParams['xtick.labelsize'] = 10.
mpl.rcParams['ytick.labelsize'] = 10.
mpl.rcParams['xtick.minor.pad'] = 4
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.direction'] = 'out'
#Git says this is patched, but it doesn't work from Pip --upgrade 26-mar-2015
#mpl.rcParams['xtick.minor.visible'] = True  

# These are the "Tableau 20" colors as RGB.  
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14),
             (255, 187, 120), (44, 160, 44), (152, 223, 138),
              (148, 103, 189),
             (197, 176, 213), (140, 86, 75), (196, 156, 148),  
             (227, 119, 194), (247, 182, 210), (127, 127, 127),
             (199, 199, 199), (188, 189, 34), (219, 219, 141),
             (23, 190, 207), (158, 218, 229),(214, 39, 40), (255, 152, 150)]  
    
# Scale the RGB values to the [0, 1] range,
# which is the format matplotlib accepts.  
for i in range(len(tableau20)): 
    r, g, b = tableau20[i]  
    tableau20[i] = (r / 255., g / 255., b / 255.)  

# Specific Plotting Params
i_leu = 0
i_delta = 2
i_heu = 4

ps = 0


    

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dir = 'data/random_sink/bad_csv/'

#file_HEU003 = dir + 'HEU1p_aq33_s_05_swu180_t1009_R5.csv'   # HEU trade on
#file_2 = dir + 'HEU0p_aq33_s_05_swu180_t1009_R5.csv' #HEU off
#file_3 = dir + 'test3_new.csv' #HEU off (repeat)
file_HEU003 = dir + 'test.csv'   # HEU trade on
file_2 = dir + 'test2.csv' #HEU off
file_3 = dir + 'test3.csv' #HEU off (repeat)


raw_data = pd.read_csv(file_HEU003) 
raw_data2 = pd.read_csv(file_2) 
raw_data3 = pd.read_csv(file_3)


    

    




---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-3-aa8c72fa0dfe> in <module>()
      9 
     10 
---> 11 raw_data = pd.read_csv(file_HEU003)
     12 raw_data2 = pd.read_csv(file_2)
     13 raw_data3 = pd.read_csv(file_3)

/Users/mbmcgarry/Library/Python/2.7/lib/python/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
    463                     skip_blank_lines=skip_blank_lines)
    464 
--> 465         return _read(filepath_or_buffer, kwds)
    466 
    467     parser_f.__name__ = name

/Users/mbmcgarry/Library/Python/2.7/lib/python/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
    239 
    240     # Create the parser.
--> 241     parser = TextFileReader(filepath_or_buffer, **kwds)
    242 
    243     if (nrows is not None) and (chunksize is not None):

/Users/mbmcgarry/Library/Python/2.7/lib/python/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
    555             self.options['has_index_names'] = kwds['has_index_names']
    556 
--> 557         self._make_engine(self.engine)
    558 
    559     def _get_options_with_defaults(self, engine):

/Users/mbmcgarry/Library/Python/2.7/lib/python/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
    692     def _make_engine(self, engine='c'):
    693         if engine == 'c':
--> 694             self._engine = CParserWrapper(self.f, **self.options)
    695         else:
    696             if engine == 'python':

/Users/mbmcgarry/Library/Python/2.7/lib/python/site-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
   1059         kwds['allow_leading_cols'] = self.index_col is not False
   1060 
-> 1061         self._reader = _parser.TextReader(src, **kwds)
   1062 
   1063         # XXX

pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:3150)()

pandas/parser.pyx in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:5772)()

IOError: File data/random_sink/bad_csv/test.csv does not exist

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LEU = raw_data[raw_data['Prototype'] == "LEU"]
delta_LEU = raw_data[raw_data['Prototype'] == "delta_LEU"]
covert_HEU = raw_data[raw_data['Prototype'] == "covert_HEU"]


    

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LEU2 = raw_data2[raw_data2['Prototype'] == "LEU"]
delta_LEU2 = raw_data2[raw_data2['Prototype'] == "delta_LEU"]
covert_HEU2 = raw_data2[raw_data2['Prototype'] == "covert_HEU"]

LEU3 = raw_data3[raw_data3['Prototype'] == "LEU"]
delta_LEU3 = raw_data3[raw_data3['Prototype'] == "delta_LEU"]
covert_HEU3 = raw_data3[raw_data3['Prototype'] == "covert_HEU"]


    

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p_leu = plt.plot(LEU['Time'],LEU[' Quantity'], label='LEU', color=tableau20[i_leu])
p_delta = plt.plot(delta_LEU['Time'],delta_LEU[' Quantity'], label='$\delta$ LEU', color=tableau20[i_delta])
p_heu = plt.plot(covert_HEU['Time'],covert_HEU[' Quantity'], label='HEU', color=tableau20[i_heu])

plt.legend(bbox_to_anchor=(0.25, 1))
plt.xlabel('Time')
plt.ylabel('Quantity')
plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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LEU_tp =(LEU[' Quantity']- LEU[' Quantity'].shift(1))
delta_LEU_tp =  delta_LEU[' Quantity']- delta_LEU[' Quantity'].shift(1)
covert_HEU_tp =  covert_HEU[' Quantity']- covert_HEU[' Quantity'].shift(1)


    

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LEU_tp2 =(LEU2[' Quantity']- LEU2[' Quantity'].shift(1))
delta_LEU_tp2 =  delta_LEU2[' Quantity']- delta_LEU2[' Quantity'].shift(1)
covert_HEU_tp2 =  covert_HEU2[' Quantity']- covert_HEU2[' Quantity'].shift(1) 

LEU_tp3 =(LEU3[' Quantity']- LEU3[' Quantity'].shift(1))
delta_LEU_tp3 =  delta_LEU3[' Quantity']- delta_LEU3[' Quantity'].shift(1)
covert_HEU_tp3 =  covert_HEU3[' Quantity']- covert_HEU3[' Quantity'].shift(1)


    

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plt.plot(LEU2['Time'],(LEU_tp2), label='Net LEU (noHEU1)', color=tableau20[i_leu+4])
#plt.plot(LEU3['Time'],(LEU_tp3), label='Net LEU (noHEU2)', color=tableau20[i_leu+6])
plt.legend(loc = 'best')
plt.xlabel('Time')
plt.ylabel('Throughput (kg)')
plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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ymin=31
ymax=35
ylim(ymin, ymax)  

plt.plot(LEU2['Time'],(LEU_tp2), label='Net LEU (noHEU)', color=tableau20[i_leu+4])
plt.plot(LEU['Time'],(LEU_tp), label='Net LEU', color=tableau20[i_leu])

plt.legend(loc='best')
plt.xlabel('Time')
plt.ylabel('Throughput (kg)')

if ps == 1:
    savefig('CR_netLEU_thruput2.png')
else:
    plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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plt.plot(delta_LEU['Time'],delta_LEU_tp, label='$\delta$ LEU', color=tableau20[i_delta])
plt.legend(bbox_to_anchor=(0.25, 1))
plt.xlabel('Time')
plt.ylabel('Throughput (kg)')
plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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plt.plot(LEU['Time'][3:],covert_HEU_tp, label='covert HEU', color=tableau20[i_heu])

plt.legend(bbox_to_anchor=(0.5, 1))
plt.xlabel('Time')
plt.ylabel('Throughput')
plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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#file_4 = dir + 'HEU03p_aq33_s_05_swu180_t100_E5_n2.csv'   # HEU trade on
file_4 = dir + 'HEU04p_aq33_s_05_swu180_t100_E5.csv'   # HEU trade on
#file_4 = dir + 'last_test.csv'   # HEU trade on

file_5 = dir + 'HEU0p_aq33_s_05_swu180_t100_E5_n5.csv' #HEU off

raw_data4 = pd.read_csv(file_4) 
raw_data5 = pd.read_csv(file_5)


    

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LEU4 = raw_data4[raw_data4['Prototype'] == "LEU"]
delta_LEU4 = raw_data4[raw_data4['Prototype'] == "delta_LEU"]
covert_HEU4 = raw_data4[raw_data4['Prototype'] == "covert_HEU"]

LEU5 = raw_data5[raw_data5['Prototype'] == "LEU"]
delta_LEU5 = raw_data5[raw_data5['Prototype'] == "delta_LEU"]
covert_HEU5 = raw_data5[raw_data5['Prototype'] == "covert_HEU"]


    

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p_leu = plt.plot(LEU4['Time'],LEU4[' Quantity'], label='LEU', color=tableau20[i_leu])
p_delta = plt.plot(delta_LEU4['Time'],delta_LEU4[' Quantity'], label='$\delta$ LEU', color=tableau20[i_delta])
p_heu = plt.plot(covert_HEU4['Time'],covert_HEU4[' Quantity'], label='HEU', color=tableau20[i_heu])

plt.legend(bbox_to_anchor=(0.25, 1))
plt.xlabel('Time')
plt.ylabel('Quantity')
plt.title("$\omega$=5 diversion on $\delta$=0.03")


    

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LEU_tp4 =(LEU4[' Quantity']- LEU4[' Quantity'].shift(1))
delta_LEU_tp4 =  delta_LEU4[' Quantity']- delta_LEU4[' Quantity'].shift(1)
covert_HEU_tp4 =  covert_HEU4[' Quantity']- covert_HEU4[' Quantity'].shift(1) 

LEU_tp5 =(LEU5[' Quantity']- LEU5[' Quantity'].shift(1))
delta_LEU_tp5 =  delta_LEU5[' Quantity']- delta_LEU5[' Quantity'].shift(1)
covert_HEU_tp5 =  covert_HEU5[' Quantity']- covert_HEU5[' Quantity'].shift(1)


    

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ymin=31
ymax=35
xmin=6
xmax=max(LEU4['Time'])
xlim(xmin, xmax)
ylim(ymin, ymax)  
alpha = 0.3
plt.plot(LEU5['Time'],(LEU_tp5), label='Natural $\delta_LEU$', color=tableau20[i_delta], alpha=alpha)
plt.plot(LEU4['Time'],(LEU_tp4), label='Declared LEU', color=tableau20[i_leu])
#plt.plot(LEU4['Time'][4:],covert_HEU_tp4*1000+5, label='covert HEU', color=tableau20[i_heu],alpha=0.2)

plt.legend(loc='best')
plt.xlabel('Time')
plt.ylabel('Throughput (kg)')

if ps == 1:
    savefig('CR_nat_dec_E5.png')
else:
    plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

In [ ]:

    

ymin=31
ymax=35
xmin=6
xmax=max(LEU4['Time'])
xlim(xmin, xmax)
ylim(ymin, ymax)  
plt.plot(LEU5['Time'],(LEU_tp5), label='Natural $\delta_LEU$', color=tableau20[i_delta])

plt.legend(loc='best')
plt.xlabel('Time')
plt.ylabel('Throughput (kg)')
plt.title("$\omega$=5 diversion on $\delta$=0.3")

if ps == 1:
    savefig('CR_nat_dec_E5.png')
else:
    plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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xlim(xmin, xmax)

plt.plot(LEU5['Time'],(delta_LEU_tp5*-1)+0.08, label='Natural $\delta_LEU$', color=tableau20[i_delta], alpha=alpha)
plt.plot(LEU4['Time'],(delta_LEU_tp4*-1)+0.08, label='Declared LEU', color=tableau20[i_leu], alpha=alpha)
plt.plot(LEU4['Time'][4:],covert_HEU_tp4*2, label='covert HEU', color=tableau20[i_heu],alpha=0.3)

plt.legend(loc='best')
plt.xlabel('Time')
plt.ylabel('$\Delta$ Throughput (***)')

if ps == 1:
    savefig('CR_test_delta_thruput2.png')
else:
    plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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xlim(xmin, xmax)

plt.plot(LEU4['Time'][4:],covert_HEU_tp4, label='covert HEU', color=tableau20[i_heu])

plt.legend(bbox_to_anchor=(0.5, 1))
plt.xlabel('Time')
plt.ylabel('Throughput')
plt.title("$\omega$=5 diversion on $\delta$=0.3")

if ps == 1:
    savefig('CR_covert_E5.png')
else:
    plt.title("$\omega$=5 diversion on $\delta$=0.3")


    

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