def load_all_results(data_dir = 'saved_interrogations', only_concs = False, **kwargs):
import corpkit
"""load every saved interrogation in data_dir into a dict"""
import os
import time
from other import load_result
from time import localtime, strftime
def get_root_note(kwargs):
if 'root' in kwargs.keys():
root = kwargs['root']
else:
root = False
if 'note' in kwargs.keys():
note = kwargs['note']
else:
note = False
return root, note
root, note = get_root_note(kwargs)
r = {}
fs = [f for f in os.listdir(data_dir) if f.endswith('.p') or os.path.isdir(os.path.join(data_dir, f))]
if note and len(fs) > 3:
note.progvar.set(0)
if len(fs) == 0:
if not root:
raise ValueError('No saved data found in %s' % data_dir)
#else:
#thetime = strftime("%H:%M:%S", localtime())
#if not only_concs:
# print '%s: No saved interrogations found in %s' % (thetime, data_dir)
#else:
# print '%s: No saved concordances found in %s' % (thetime, data_dir)
return
l = 0
import pandas
for index, finding in enumerate(fs):
try:
tmp = load_result(finding, loaddir = data_dir, only_concs = only_concs)
if type(tmp) != pandas.core.frame.DataFrame:
if not tmp:
continue
r[os.path.splitext(finding)[0]] = tmp
time = strftime("%H:%M:%S", localtime())
print '%s: %s loaded as %s.' % (time, finding, os.path.splitext(finding)[0])
l += 1
except:
time = strftime("%H:%M:%S", localtime())
print '%s: %s failed to load. Try using load_result to find out the matter.' % (time, finding)
if note and len(fs) > 3:
note.progvar.set((index + 1) * 100.0 / len(fs))
if root:
root.update()
time = strftime("%H:%M:%S", localtime())
print '%s: %d interrogations loaded from %s.' % (time, l, os.path.basename(data_dir))
return r
def load_all_results(data_dir = 'data/saved_interrogations'):
"""load every saved interrogation in data_dir into a dict"""
import os
import time
from corpkit.other import load_result
from time import localtime, strftime
r = {}
fs = [f for f in os.listdir(data_dir) if f.endswith('.p')]
if len(fs) == 0:
raise ValueError('No results found in %s' % datadir)
for finding in fs:
try:
r[os.path.splitext(finding)[0]] = load_result(finding, loaddir = data_dir)
time = strftime("%H:%M:%S", localtime())
print '%s: %s loaded as %s.' % (time, finding, os.path.splitext(finding)[0])
except:
time = strftime("%H:%M:%S", localtime())
print '%s: %s failed to load. Try using load_result to find out the matter.' % (time, finding)
return r
# <markdowncell>
# Those already proficient with Python can use [Pandas' `plot()` function](http://pandas.pydata.org/pandas-docs/stable/visualization.html) if they like
# <markdowncell>
# Another neat thing you can do is save the results of an interrogation, so they don't have to be run the next time you load this notebook:
# <codecell>
# specify what to save, and a name for the file.
from corpkit import save_result, load_result
save_result(allwords, 'allwords')
# <markdowncell>
# You can then load these results:
# <codecell>
fromfile_allwords = load_result('allwords')
fromfile_allwords.totals
# <markdowncell>
# ... or erase them from memory:
# <codecell>
fromfile_allwords = None
# fromfile_allwords
# <markdowncell>
# ### `quickview()`
# <markdowncell>
# `quickview()` is a function that quickly shows the n most frequent items in a list. Its arguments are:
def quickview(results, n = 25):
"""view top n results of results.
:param results: Interrogation/edited result to view
:type results: corpkit.interrogation/pandas.core.frame.DataFrame
:param n: Show top *n* results
:type n: int
"""
import corpkit
import pandas
import numpy
import os
# handle dictionaries too:
dictpath = 'dictionaries'
savedpath = 'saved_interrogations'
# too lazy to code this properly for every possible data type:
if n == 'all':
n = 9999
if type(results) == str:
if os.path.isfile(os.path.join(dictpath, results)):
import pickle
from collections import Counter
unpickled = pickle.load(open(os.path.join(dictpath, results), 'rb'))
print '\nTop %d entries in %s:\n' % (n, os.path.join(dictpath, results))
for index, (w, f) in enumerate(unpickled.most_common(n)):
fildex = '% 3d' % index
print '%s: %s (n=%d)' %(fildex, w, f)
return
elif os.path.isfile(os.path.join(savedpath, results)):
from corpkit import load_result
print '\n%s loaded temporarily from file:\n' % results
results = load_result(results)
else:
raise ValueError('File %s not found in saved_interrogations or dictionaries')
if 'interrogation' in str(type(results)):
clas = results.query['function']
if clas == 'interrogator':
datatype = results.results.iloc[0,0].dtype
if datatype == 'int64':
option = 'total'
else:
option = '%'
if results.query['query'] == 'keywords':
option = 'keywords'
elif results.query['query'] == 'ngrams':
option = 'ngrams'
try:
results_branch = results.results
resbranch = True
except AttributeError:
resbranch = False
results_branch = results
elif clas == 'editor':
# currently, it's wrong if you edit keywords! oh well
datatype = results.results.iloc[0,0].dtype
if results.query['just_totals']:
resbranch = False
if results.results.dtype == 'int64':
option = 'total'
else:
option = '%'
results_branch = results.results
else:
if datatype == 'int64':
option = 'total'
else:
option = '%'
try:
results_branch = results.results
resbranch = True
except AttributeError:
resbranch = False
if type(results) == pandas.core.frame.DataFrame:
results_branch = results
resbranch = True
if type(results.iloc[0,0]) == numpy.int64:
option = 'total'
else:
option = '%'
elif type(results) == pandas.core.series.Series:
resbranch = False
results_branch = results
if type(results.iloc[0]) == numpy.int64:
option = 'total'
else:
option = '%'
if results.name == 'keywords':
option = 'series_keywords'
if resbranch:
the_list = list(results_branch)[:n]
else:
the_list = list(results_branch.index)[:n]
for index, w in enumerate(the_list):
fildex = '% 3d' % index
if option == 'keywords':
print '%s: %s' %(fildex, w)
elif option == '%' or option == 'ratio':
if 'interrogation' in str(type(results)):
tot = results.totals[w]
totstr = "%.3f" % tot
print '%s: %s (%s%%)' % (fildex, w, totstr)
else:
print '%s: %s' % (fildex, w)
elif option == 'series_keywords':
tot = results_branch[w]
print '%s: %s (k=%d)' %(fildex, w, tot)
else:
if resbranch:
tot = sum(i for i in list(results_branch[w]))
else:
tot = results_branch[w]
print '%s: %s (n=%d)' %(fildex, w, tot)
def quickview(results, n = 25):
"""view top n results of results.
Ideally, pass it interrogator() or plotter output. It will also accept DatFrames
or Series (i.e. .results or .totals branches."""
import corpkit
import pandas
import numpy
import os
# handle dictionaries too:
dictpath = 'data/dictionaries'
savedpath = 'data/saved_interrogations'
if type(results) == str:
if os.path.isfile(os.path.join(dictpath, results)):
import pickle
from collections import Counter
unpickled = pickle.load(open(os.path.join(dictpath, results), 'rb'))
print '\nTop %d entries in %s:\n' % (n, os.path.join(dictpath, results))
for index, (w, f) in enumerate(unpickled.most_common(n)):
fildex = '% 3d' % index
print '%s: %s (n=%d)' %(fildex, w, f)
return
elif os.path.isfile(os.path.join(savedpath, results)):
from corpkit import load_result
print '\n%s loaded temporarily from file:\n' % results
results = load_result(results)
else:
raise ValueError('File %s not found in data/saved_interrogations or data/dictionaries')
if 'interrogation' in str(type(results)):
clas = results.query['function']
if clas == 'interrogator':
datatype = results.query['datatype']
if datatype == 'float64':
option = 'total'
else:
option = '%'
if results.query['query'] == 'keywords':
option = 'keywords'
elif results.query['query'] == 'ngrams':
option = 'ngrams'
try:
results_branch = results.results
resbranch = True
except AttributeError:
resbranch = False
results_branch = results
elif clas == 'editor':
# currently, it's wrong if you edit keywords! oh well
datatype = results.query['datatype']
if results.query['just_totals']:
resbranch = False
if results.results.dtype == 'int64':
option = 'total'
else:
option = '%'
results_branch = results.results
else:
if datatype == 'int64':
option = 'total'
else:
option = '%'
try:
results_branch = results.results
resbranch = True
except AttributeError:
resbranch = False
if type(results) == pandas.core.frame.DataFrame:
results_branch = results
resbranch = True
if type(results.iloc[0][0]) == numpy.int64:
option = 'total'
else:
option = '%'
elif type(results) == pandas.core.series.Series:
resbranch = False
results_branch = results
if type(results.iloc[0]) == numpy.int64:
option = 'total'
else:
option = '%'
if results.name == 'keywords':
option = 'series_keywords'
if resbranch:
the_list = list(results_branch)[:n]
else:
the_list = list(results_branch.index)[:n]
for index, w in enumerate(the_list):
fildex = '% 3d' % index
if option == 'keywords':
print '%s: %s' %(fildex, w)
elif option == '%' or option == 'ratio':
print '%s: %s' % (fildex, w)
elif option == 'series_keywords':
tot = results_branch[w]
print '%s: %s (kq=%d)' %(fildex, w, tot)
else:
if resbranch:
tot = sum(i for i in list(results_branch[w]))
else:
tot = results_branch[w]
print '%s: %s (n=%d)' %(fildex, w, tot)
