Example #1
0
Keywords:       
Calls   :       os, numpy, matplotlib, pandas
                config, savefig, cmes
Written :       Jason P Byrne, STFC/RAL Space, Dec 2015 ([email protected])
Revisions:
2015-12-11 JPB : 
'''

import os
import numpy as np
import matplotlib.pyplot as plt
import config
from savefig import save
from cmes import cdaw, hicat

df_cdaw = cdaw().convert_objects(convert_numeric=True)
df_hicat = hicat().convert_objects(convert_numeric=True)

#global variables
speeds_label = "Speed ($km s^{-1}$)"
ledge_sz = 10

cols_hist = ['cpa','mpa','width','lin_speed','quad_speed_init','quad_speed_20',\
        'quad_speed_final','accel']

# Split speeds by year
def speeds_datetime():
        import datetime
        import matplotlib.ticker as ticker
        time_format_cdaw = "%Y/%m/%dT%H:%M:%S"
        time_format_hicat = "%Y-%m-%dT%H:%MZ"
Example #2
0
Keywords:       
Calls   :       os, numpy, matplotlib, pandas
                config, savefig, cmes
Written :       Jason P Byrne, STFC/RAL Space, Dec 2015 ([email protected])
Revisions:
2015-12-11 JPB : 
'''

import os
import numpy as np
import matplotlib.pyplot as plt
import config
from savefig import save
from cmes import cdaw, hicat

df_cdaw = cdaw().convert_objects(convert_numeric=True)
df_hicat = hicat().convert_objects(convert_numeric=True)

#global variables
speeds_label = "Speed ($km s^{-1}$)"
ledge_sz = 10

cols_hist = ['cpa','mpa','width','lin_speed','quad_speed_init','quad_speed_20',\
        'quad_speed_final','accel']


# Split speeds by year
def speeds_datetime():
    import datetime
    import matplotlib.ticker as ticker
    time_format_cdaw = "%Y/%m/%dT%H:%M:%S"
#!/usr/bin/env python

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import config

from savefig import save

from cmes import cdaw,hicactus,hicat

# Call the catalog functions:
df_cdaw = cdaw()
df_hicact_a = hicactus('A')
df_hicact_b = hicactus('B')
df_hicat = hicat()

# Convert strings to numerics
# df = df.convert_objects(convert_numeric=True)
# Drop NaNs
# df.dropna(axis='rows',how='any',inplace=True)
df_cdaw.describe()
# Generate some initial plots for CDAW
#df_cdaw.hist()
#save(path=os.path.join(config.wp3_path,"cdaw_cme_catalog/cdaw_hist"),verbose=True)
#plt.show()


hicact_a_speeds = df_hicact_a[['v']]
hicact_b_speeds = df_hicact_b[['v']]