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Commit 505679ce authored by Qusai Al Shidi's avatar Qusai Al Shidi 💬
Browse files

io.read_gm_log does not rely on pandas anymore instead it gives a dict

parent 7a4e7d22
......@@ -170,47 +170,63 @@ def write_imf_input(data, outfilename="IMF.dat", enable_rb=True, **kwargs):
+ '\n')
def read_gm_log(filename, colnames=None, index_by_time=True):
"""Make a pandas.DataFrame out of the Dst indeces outputted
def read_gm_log(filename, colnames=None, index_time=True):
"""Make a dictionary out of the indeces outputted
from the GM model log.
Args:
filename (str): The filename as a string.
colnames ([str]): Supply the name of the columns
index_by_time (bool): Change the index to a time index
colnames ([str]): (default: None) Supply the name of the columns.
If None it will use second line
of log file.
index_time (bool): (default: True) Make a column of dt.datetime objects
in dictionary key 'Time [UT]'.
Returns:
pandas.DataFrame of the log file
Dictionary of the log file
Examples:
To plot AL and Dst get the log files
```
geo = swmfpy.read_gm_log("run/GM/IO2/geoindex_e20140215-100500.log")
dst = swmfpy.read_gm_log("run/GM/IO2/log_e20140215-100500.log")
```
geo = swmfpy.io.read_gm_log('run/GM/IO2/geoindex_e20140215-100500.log')
dst = swmfpy.io.read_gm_log('run/GM/IO2/log_e20140215-100500.log')
Then plot using pandas features
# Plot AL indeces
plt.plot(geo['Time [UT]', geo['AL'])
```
dst["dst_sm"].plot.line()
geo["AL"].plot.line()
```
"""
import pandas as pd
"""
# If column names were not specified
if not colnames:
with open(filename, 'r') as logfile:
# Usually the names are in the second line
logfile.readline()
return_data = {}
with open(filename, 'r') as logfile:
# Find column names and initialize
description = logfile.readline()
return_data['description'] = description
# Usually the names are in the second line
if not colnames:
colnames = logfile.readline().split()
data = pd.read_fwf(filename, header=None, skiprows=2, names=colnames)
if index_by_time:
data.set_index(pd.to_datetime({'year': data['year'],
'month': data['mo'],
'day': data['dy'],
'h': data['hr'],
'm': data['mn'],
's': data['sc']}),
inplace=True)
data.index.names = ['Time [UT]']
return data
for col in colnames:
return_data[col] = []
# Fill data dictionary
for line_num, line in enumerate(logfile):
if line_num > 2: # First two lines are usually metadata
for col, data in enumerate(line.split()):
return_data[colnames[col]].append(data)
# datetime index
if index_time:
return_data['Time [UT]'] = []
for row, year in enumerate(return_data[colnames[1]]):
return_data['Time [UT]'].append(
dt.datetime(int(year),
int(return_data[colnames[2]][row]), # month
int(return_data[colnames[3]][row]), # day
int(return_data[colnames[4]][row]), # hour
int(return_data[colnames[5]][row]), # min
int(return_data[colnames[6]][row]), # sec
int(return_data[colnames[7]][row]))) # ms
return return_data
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