def load_from_file(self, filename, append_data=False):
loaded_data_frame = pandas.load(filename)
if append_data:
self.data_frame = pandas.concat(
[self.data_frame, loaded_data_frame])
else:
self.data_frame = pandas.load(filename)
def load_data(locking_event, after_trial_type, bins, meth):
"""Load the appropriate dataset given by locking_event and after_trial_type
Returns: suffix, all_event_latencies, binneds
all_event_latencies is a series with multi-index (
block, gng, event, ulabel)
"""
suffix = '_lock_%s_%s' % (locking_event, after_trial_type)
dfoldeds = pandas.load('dfoldeds' + suffix)
times_distr = pandas.load('times_distrs' + suffix)
# Bin each individual sname from each folded
binneds = dfoldeds['dfolded'].apply(
lambda dfolded: kkpandas.Binned.from_dict_of_folded(
dfolded, bins=bins, meth=meth))
# Convert times_distr to a series keyed by (block, gng, event, ulabel)
# instead of by (ulabel, group, event)
all_event_latencies = times_distr.copy()
all_event_latencies.index = pandas.MultiIndex.from_tuples([
(idx[1][:2], idx[1][-2:], idx[2], idx[0])
for idx in all_event_latencies.index],
names=['block', 'gng', 'event', 'ulabel'],)
return suffix, all_event_latencies, binneds
def load_from_file(self, filename, append_data=False):
loaded_data_frame = pandas.load(filename)
if append_data:
self.data_frame = pandas.concat([self.data_frame,
loaded_data_frame])
else:
self.data_frame = pandas.load(filename)
def load_data(locking_event, after_trial_type, bins, meth):
"""Load the appropriate dataset given by locking_event and after_trial_type
Returns: suffix, all_event_latencies, binneds
all_event_latencies is a series with multi-index (
block, gng, event, ulabel)
"""
suffix = '_lock_%s_%s' % (locking_event, after_trial_type)
dfoldeds = pandas.load('dfoldeds' + suffix)
times_distr = pandas.load('times_distrs' + suffix)
# Bin each individual sname from each folded
binneds = dfoldeds['dfolded'].apply(
lambda dfolded: kkpandas.Binned.from_dict_of_folded(
dfolded, bins=bins, meth=meth))
# Convert times_distr to a series keyed by (block, gng, event, ulabel)
# instead of by (ulabel, group, event)
all_event_latencies = times_distr.copy()
all_event_latencies.index = pandas.MultiIndex.from_tuples(
[(idx[1][:2], idx[1][-2:], idx[2], idx[0])
for idx in all_event_latencies.index],
names=['block', 'gng', 'event', 'ulabel'],
)
return suffix, all_event_latencies, binneds
def create_data_df(ts,wts):
ts = pd.load('../Data/ts_data')
#wts = pd.load('../Data/wts_data')
wts = pd.load('../Data/ts_humidex')
prevday_ts = ts.tshift(1,'D')
prevday_avg = prevday_ts.resample('D',how='mean')
prevday_avg = prevday_avg.asfreq('15Min',method='pad')
prevweek_ts = ts.tshift(7,'D')
#change min_date of all timeseries to the prevweek_ts date
min_date = max(min(ts.index),min(prevweek_ts.index),min(prevday_ts.index),min(wts.index))
max_date = max(prevday_ts.index)
ts = ts[min_date:max_date]
wts = wts[min_date:max_date]
wts[wts<-20] = None
wts = wts.interpolate()
prevday_ts = prevday_ts[min_date:]
prevday_avg = prevday_avg[min_date:]
prevweek_ts = prevweek_ts[min_date:]
all_covs = pd.DataFrame({'load':ts,'prevday_load':prevday_ts,'prevweek_ts':prevweek_ts,'prevday_avg':prevday_avg,'weather':wts},index=prevday_ts.index)
hr = pd.Series(ts.index.map(lambda x: x.hour+(float(x.minute)/60)),index=ts.index)
dod = pd.Series(ts.index.map(lambda x: int('%d%03d'%(x.year,x.dayofyear))),index=ts.index)
mydf = pd.concat([ts,dod,hr],axis=1)
mydf.columns=['ts','dod','hr']
mydf = mydf.pivot(columns='dod',index='hr',values='ts')
mywdf = pd.concat([wts,dod,hr],axis=1)
mywdf.columns = ['wts','dod','hr']
mywdf = mywdf.drop_duplicates(cols=['hr','dod'],take_last=True)
mywdf = mywdf.pivot(columns='dod',index='hr',values='wts')
return mydf,mywdf
def practice_two():
frame = pd.read_csv('ch06/ex1.csv') # 读
frame.save('ch06/frame_pickle') # 写 !!!出现错误，无法存储
pd.load('ch06/frame_pickle') # 读
# 使用HDF5格式
store = pd.HDFStore('mydata.h5')
store['obj1'] = frame
store['obj1_col'] = frame['a']
# 读取Microsoft Excel文件 使用xlrd和openpyxl包
xls_file = pd.ExcelFile('data.xls') # 传入文件
table = xls_file.parse('Sheet1')
pass
def main():
os.chdir("../../")
# Load the DataFrame representing the MovieLens subset
ratings=pd.load('input_data/ratings_train.pda')
ratings = ratings.dropna()
nratings = ratings.shape[0]
try:
itemFilter = CFilter_item(ratings)
item_cf_ratings = itemFilter.get_cf_rating(ratings)
# Compute average deviation from rating on those ratings
avgDeviation = np.sum(np.abs((item_cf_ratings - ratings['rating']))) / len(ratings)
print "Average deviation from rating: %.4f" %(avgDeviation)
# Dump the ratings to disk for easy future access
fp = open("proc_data/item_item_ratings_2.pda", "wb")
pkl.dump(item_cf_ratings, fp)
fp.close()
print "This concludes the main method. Buh-bye!"
except LogicalError as l:
print "A logical error occurred: %s" %(l)
except DatasetError as d:
print "A dataset-related error occurred: %s" %(d)
except Exception as e:
print "An exception occurred: " + str(e)
def readPkl():
currDir = os.getcwd()
files = os.path.join(currDir, 'pkl', 'BasicFeatures', '*.pkl')
for src in glob.glob(files):
df = pd.load(src)
print df.index[:10]
print df.index[0].strftime('%Y%m%d')
def display_progress_sparse():
progress = pd.load('results/progress_sparse.pkl')
for algo in ['batch', 'bb', 'lbfgs']:
for i in range(1):
x = progress.loc[algo+'_x_dense_'+str(i)]['time']
y = progress.loc[algo+'_x_dense_'+str(i)]['f-f_min']
plt.plot(x, y, 'r', linewidth=2, label='x dense')
x = progress.loc[algo+'_z_dense_'+str(i)]['time']
y = progress.loc[algo+'_z_dense_'+str(i)]['f-f_min']
plt.plot(x, y, 'g', linewidth=2, label='z dense')
x = progress.loc[algo+'_x_sparse_'+str(i)]['time']
y = progress.loc[algo+'_x_sparse_'+str(i)]['f-f_min']
plt.plot(x, y, '--r', linewidth=2, label='x_sparse')
x = progress.loc[algo+'_z_sparse_'+str(i)]['time']
y = progress.loc[algo+'_z_sparse_'+str(i)]['f-f_min']
plt.plot(x, y, '--g', linewidth=2, label='z_sparse')
#plt.xscale('log')
plt.yscale('log')
plt.legend(loc=0)
plt.ylabel('log10 f - f_min', fontsize=16)
plt.xlabel('time in seconds', fontsize=16)
#plt.title(algo+' experiment '+str(i), fontsize=16)
plt.title(algo, fontsize=16)
plt.show()
def display_progress():
progress = pd.load('results/progress.pkl')
for algo in ['batch', 'bb', 'lbfgs']:
for i in range(1):
data = progress.loc[algo+'_x_'+str(i)]
x = np.array(data['time'])
y = np.array(data['f-f_min'])
x, log_y, alpha = clean_progress(x, y)
plt.plot(x, log_y, 'r', label=algo+' in x', linewidth=2)
plt.plot(x, alpha*x + log_y[0], '--r', label='linear fit', linewidth=2)
data = progress.loc[algo+'_z_'+str(i)]
x = np.array(data['time'])
y = np.array(data['f-f_min'])
x, log_y, alpha = clean_progress(x, y)
plt.plot(x, log_y, 'g', label=algo+' in z', linewidth=2)
plt.plot(x, alpha*x + log_y[0], '--g', label='linear fit', linewidth=2)
plt.legend(loc=0)
plt.xlabel('time (s)', fontsize=20)
plt.ylabel('f-f_min', fontsize=20)
plt.title(algo+' experiment '+str(i), fontsize=20)
plt.show()
def __init__(self, table):
"""
table : string
Expect to be a csv file with the first column the name of the item
"""
ext = os.path.splitext(table)[-1]
if ext == '.csv':
self.df = pd.read_csv(table, index_col=0)
elif ext == '.pickle':
self.df = pd.load(table)
else:
raise Exception('table extension not .csv or .pickle')
self.cols = self.df.columns
self.index = self.df.index
self.df['name'] = self.index
# this is the list of types that I've found so far that pandas.read_csv generates
self.types = [
dict(float64='Number',
float='Number',
int64='Number',
object='Value',
bool='bool')[str(t)] for t in self.df.dtypes
]
print 'Loaded CSV file %s: found %d items, %d columns' % (
table, len(self.df), len(self.cols))
def get_annual_total_net_assets(force_db_read=False, df_file = 'monthlytna.pandas'):
""" gets average total net assets per fund per year
NOTE: This is not used in the current iteration of research
"""
# caching to make things faster most of the time
if not force_db_read and os.path.isfile(df_file):
print "Loading file from cache:",df_file
return pd.load(df_file)
#otherwise read from db
try:
print "Getting Total Net Assets from database"
con=None
con = lite.connect(config['db_path'])
cur = con.cursor()
sql = """select crsp_fundno, substr(caldt,1,4) as year, mtna from MONTHLY_TNA where mtna <> '';"""
cur.execute(sql)
data = cur.fetchall()
print "Done getting Total Net Assets"
df = pd.DataFrame(list(data),columns=['FundNo','Year','mtna'],dtype=np.float64)
df=df.replace({'mtna':-99.0}, value=np.nan)
df.dropna(subset=['mtna'])
grouped = df.groupby(['FundNo','Year'])
tna_means = grouped.mean()
print "Saving monthly TNA dataframe for next time as",df_file
tna_means.save(df_file)
return tna_means
except lite.Error, e:
print "Error %s:" % e.args[0]
sys.exit(1)
def main(args):
df = pd.load(args.df)
estimators = [('reduce_dim', KernelPCA(kernel='linear')),
('knc', KNeighborsClassifier(warn_on_equidistant=False))]
clf = Pipeline(estimators)
params = dict(reduce_dim__n_components=[2, 3, 5, 8, 10, 25, 50, 100])
grid_search = GridSearchCV(clf, param_grid=params, cv=3)
best = 0.0
while True:
random_seed = int(time.time() * 1000)
random.seed(random_seed)
df_l, y_l, df_t = split_df(df, 0.33)
try:
grid_search.fit(df_l, y_l)
except ValueError:
# Most likely wrong number of classes in cross validation set
continue
y_t = integer_labels(df_t)
c = grid_search.predict(df_t)
score, c = compare_clusters(c, y_t)
if score > best:
best = score
print('{} {}'.format(random_seed, best))
with open(args.out, 'a') as fh:
fh.write('{} {}\n'.format(random_seed, best))
def calc_all_fund_r2(fund_returns,
asset_returns,
force_calc=False,
df_file='r2_all_funds.pandas'):
""" Calculates R2 across all funds and asset class benchmarks, returns dataframe of results """
# caching to make things faster most of the time
if not force_calc and os.path.isfile(df_file):
print "Loading R2 file from cache:", df_file
return pd.load(df_file)
#otherwise re-calc
asset_r2_results = {}
fund_list = get_fund_list(fund_returns)
asset_list = asset_classes
# dataframe to hold our results, index by fund, columns will be asset classes
r2_df = pd.DataFrame(index=fund_list)
for asset_series in asset_returns:
print '***** Calculating R2 for asset class', asset_series.name
for fund in fund_list:
#reindex as datetime so we can resample to EOM dates and join with the benchmark
f = fund_returns.ix[fund]
f.index = pd.DatetimeIndex(f.index)
f = f.resample(
'M', how='prod'
) # just to normalize dates, shouldn't change data points
r2 = calc_r2(f.join(asset_series, how='outer'))
asset_r2_results[fund] = r2
print 'R2(%s, fund %s) = %s' % (asset_series.name, fund, r2)
r2_df[asset_series.name] = pd.Series(asset_r2_results)
asset_r2_results = {}
print 'Saving R2 results to', df_file
r2_df.save(df_file)
return r2_df
def data(self):
""" Returns a pandas.DataFrame of data, or None if not available. """
with transaction.commit_on_success():
self.refresh()
if not self.status == Job.COMPLETE:
raise ValueError("Job not complete, no data available")
self.reference("data()")
e = None
try:
logger.debug("%s looking for data file: %s" %
(str(self), self.datafile()))
if os.path.exists(self.datafile()):
df = pandas.load(self.datafile())
logger.debug("%s data loaded %d rows from file: %s" %
(str(self), len(df), self.datafile()))
else:
logger.debug("%s no data, missing data file: %s" %
(str(self), self.datafile()))
df = None
except Exception as e:
logger.error("Error loading datafile %s for %s" %
(self.datafile(), str(self)))
logger.error("Traceback:\n%s" % e)
finally:
self.dereference("data()")
if e:
raise e
return df
def data(self):
""" Returns a pandas.DataFrame of data, or None if not available. """
with transaction.commit_on_success():
self.refresh()
if not self.status == Job.COMPLETE:
raise ValueError("Job not complete, no data available")
self.reference("data()")
e = None
try:
logger.debug("%s looking for data file: %s" %
(str(self), self.datafile()))
if os.path.exists(self.datafile()):
df = pandas.load(self.datafile())
logger.debug("%s data loaded %d rows from file: %s" %
(str(self), len(df), self.datafile()))
else:
logger.debug("%s no data, missing data file: %s" %
(str(self), self.datafile()))
df = None
except Exception as e:
pass
finally:
self.dereference("data()")
if e:
raise e
return df
def get_annual_total_net_assets(force_db_read=False,
df_file='monthlytna.pandas'):
""" gets average total net assets per fund per year
NOTE: This is not used in the current iteration of research
"""
# caching to make things faster most of the time
if not force_db_read and os.path.isfile(df_file):
print "Loading file from cache:", df_file
return pd.load(df_file)
#otherwise read from db
try:
print "Getting Total Net Assets from database"
con = None
con = lite.connect(config['db_path'])
cur = con.cursor()
sql = """select crsp_fundno, substr(caldt,1,4) as year, mtna from MONTHLY_TNA where mtna <> '';"""
cur.execute(sql)
data = cur.fetchall()
print "Done getting Total Net Assets"
df = pd.DataFrame(list(data),
columns=['FundNo', 'Year', 'mtna'],
dtype=np.float64)
df = df.replace({'mtna': -99.0}, value=np.nan)
df.dropna(subset=['mtna'])
grouped = df.groupby(['FundNo', 'Year'])
tna_means = grouped.mean()
print "Saving monthly TNA dataframe for next time as", df_file
tna_means.save(df_file)
return tna_means
except lite.Error, e:
print "Error %s:" % e.args[0]
sys.exit(1)
def process_wave_height(awac_path):
awac_path = os.path.normpath(awac_path)
path = os.path.sep.join(awac_path.split(os.path.sep)[:-1])
os.chdir(path)
awac_file_name = awac_path.split(os.path.sep)[-1:][0]
wave_height_df = pd.load(awac_file_name)
get_stats_from_df(wave_height_df, "wave_height_decibar", path)
def chewsmet_txt(path, title):
import pandas
metdf = pandas.load(path + title + '.df')
# open all file end with .df
#metdf=pandas.load('/Users/Eating/Documents/ATM_R/python program/20150615_7.df')
#print metdf['Wdir'][-1]
#raw_input()
#print metdf.index
#raw_input()
metdfFile = open(path + title + '.txt', 'w')
#metdfFile=open('/Users/Eating/Documents/ATM_R/python program/20150615_7.txt','w')
metdfFile.write(
'Date_Time, Wind Speed, Wind Dir, Temperature, WaterPress, Humidity\n '
)
for i in range(0, len(metdf.index)):
##print metdf['Wdir'][i]
#print metdf['Wspd'][i]
metdfFile.write(
str(metdf.index[i]) + ', ' + str(metdf['Wspd'][i]) + ', ' +
str(metdf['Wdir'][i]) + ',' + str(metdf['temp'][i]) + ',' +
str(metdf['H20Pres'][i]) + ',' + str(metdf['humidity'][i]) + '\n')
#raw_input()
metdfFile.close()
print('done')
def readPkl():
currDir = os.getcwd()
files = os.path.join(currDir, 'pkl', 'BasicFeatures', '*.pkl')
for src in glob.glob(files):
df = pd.load(src)
print df.index[:10]
print df.index[0].strftime('%Y%m%d')
def load_quote(currency, path=NETFOUNDS_QUOTES_PATH):
try:
return pd.load(path + '/{}.dat'.format(currency))
except FileNotFoundError:
return None
except Exception as e:
raise(e)
def load_quote(currency, path=NETFOUNDS_QUOTES_PATH):
try:
return pd.load(path + '/{}.dat'.format(currency))
except FileNotFoundError:
return None
except Exception as e:
raise (e)
def get_style_bucket_funds(fund_returns, force_bucket=False, bucket_file='bucketed_style.pandas'):
""" Creates a dataframe of funds that by style mapped to each asset class"""
if not force_bucket and os.path.isfile(bucket_file):
print "Loading bucketed style file from cache:",bucket_file
return pd.load(bucket_file)
#otherwise create the bucket file
print "Bucketing by style"
fund_styles = get_fund_styles()
# apply mapping of our asset classes to styles
# [[put in metamappings.py]]
asset_style_results = {}
fund_list = get_fund_list(fund_returns)
asset_list = asset_classes
# dataframe to hold our results, index by fund, columns will be asset classes
style_df=pd.DataFrame(index=fund_list)
for asset_class in asset_classes:
print '***** Looking up styles for asset class',asset_class
styles = crsp_style_mapping[asset_class]
#loop through on the funds we have styles for
for fund in list(set(fund_list).intersection(list(fund_styles.index))):
if fund_styles.ix[fund]['StyleCode'] in styles:
asset_style_results[fund] = 1
style_df[asset_class] = pd.Series(asset_style_results)
asset_style_results = {}
# take only the matching styles, and drop any rows with no assets highly correlated
bucketed_df=style_df[style_df.apply(lambda x: x == 1, axis=1)].dropna(how='all')
# save for next time
bucketed_df.save(bucket_file)
return bucketed_df
def main():
os.chdir("../../")
# Load the DataFrame representing the MovieLens subset
ratings=pd.load('input_data/ratings_train.pda')
ratings = ratings.dropna()
nratings = ratings.shape[0]
# Load the CFiltering_item object
fp = open("proc_data/cfilter_object.pda", "rb")
cf3 = pkl.load(fp)
fp.close()
try:
# we need to make sure everything's ok with our code
# so we will do some tests on dimished MovieLens data
item_cf_ratings = cf3.get_cf_rating(ratings.ix[:10000,:])
# Compute squared loss on those ratings
avgSquaredLoss = np.sum(np.square(item_cf_ratings - ratings.ix[:10000, :]['rating'])) / len(ratings)
print avgSquaredLoss # 1.59836284936 on the first 10000 items!
except LogicalError as l:
print "A logical error occurred: %s" %(l)
except DatasetError as d:
print "A dataset-related error occurred: %s" %(d)
except Exception as e:
print "An exception occurred: " + str(e)
def chewsmet_txt(path,title):
import pandas
metdf=pandas.load(path+title+'.df')
# open all file end with .df
#metdf=pandas.load('/Users/Eating/Documents/ATM_R/python program/20150615_7.df')
#print metdf['Wdir'][-1]
#raw_input()
#print metdf.index
#raw_input()
metdfFile=open(path+title+'.txt','w')
#metdfFile=open('/Users/Eating/Documents/ATM_R/python program/20150615_7.txt','w')
metdfFile.write('Date_Time, Wind Speed, Wind Dir, Temperature, WaterPress, Humidity\n ')
for i in range(0,len(metdf.index)):
##print metdf['Wdir'][i]
#print metdf['Wspd'][i]
metdfFile.write(str(metdf.index[i])+', '+str(metdf['Wspd'][i])+', '+str(metdf['Wdir'][i])+','+str(metdf['temp'][i])+','+str(metdf['H20Pres'][i])+','+str(metdf['humidity'][i])+'\n')
#raw_input()
metdfFile.close()
print('done')
def load_assoc(self, fromdf=None):
if fromdf is not None:
print 'loading associations from file %s' % fromdf
self.df['altassoc'] = pd.load(fromdf)
else:
print 'using associations found in sourceinfo'
associations = self.df.associations if fromdf is None else self.df.altassoc
probfun = lambda x: x['prob'][0] if not pd.isnull(x) else 0
self.df['aprob'] = np.array([probfun(assoc) for assoc in associations])
self.df['acat'] = np.array([
assoc['cat'][0] if not pd.isnull(assoc) else 'unid'
for assoc in associations
])
self.df['aname'] = np.array([
assoc['name'][0] if not pd.isnull(assoc) else 'unid'
for assoc in associations
])
self.df['aang'] = np.array([
assoc['ang'][0] if not pd.isnull(assoc) else np.nan
for assoc in associations
])
self.df['adeltats'] = np.array([
assoc['deltats'][0] if not pd.isnull(assoc) else np.nan
for assoc in associations
])
self.df10 = self.df.ix[self.df.ts > 10]
print 'associated: %d/%d' % (sum(self.df10.aprob > 0.8), len(
self.df10))
def calc_all_fund_r2(fund_returns, asset_returns, force_calc=False, df_file='r2_all_funds.pandas'):
""" Calculates R2 across all funds and asset class benchmarks, returns dataframe of results """
# caching to make things faster most of the time
if not force_calc and os.path.isfile(df_file):
print "Loading R2 file from cache:",df_file
return pd.load(df_file)
#otherwise re-calc
asset_r2_results = {}
fund_list = get_fund_list(fund_returns)
asset_list = asset_classes
# dataframe to hold our results, index by fund, columns will be asset classes
r2_df=pd.DataFrame(index=fund_list)
for asset_series in asset_returns:
print '***** Calculating R2 for asset class',asset_series.name
for fund in fund_list:
#reindex as datetime so we can resample to EOM dates and join with the benchmark
f = fund_returns.ix[fund]
f.index = pd.DatetimeIndex(f.index)
f = f.resample('M', how='prod') # just to normalize dates, shouldn't change data points
r2 = calc_r2(f.join(asset_series,how='outer'))
asset_r2_results[fund] = r2
print 'R2(%s, fund %s) = %s' % (asset_series.name,fund,r2)
r2_df[asset_series.name] = pd.Series(asset_r2_results)
asset_r2_results = {}
print 'Saving R2 results to',df_file
r2_df.save(df_file)
return r2_df
def loadDf(filename):
'''
load '.df' files
ret: DataFrame
'''
temp_pickle = pandas.load(filename)
print temp_pickle
return temp_pickle
def test_wap_dataframe(self):
print("TestParseWap")
try:
parse_wap.load(awac_folder_path + 'test_data.wap')
except WindowsError:
print("Load wap Files failed")
wap_dataframe = pd.load('test_data_wap_df')
self.assertEqual(len(wap_dataframe),wad_records)
def load_quote(currency):
try:
return pd.load(DUKASCOPY_QUOTES_PATH + '/{}.dat'.format(currency))
except FileNotFoundError as e:
print(e)
return None
except Exception as e:
raise (e)
def filter_cz_cities(input="geography-first-all-2.pd", output=None):
data = pd.load(input)
places = get_maps()["Czech Rep.-city"]
filtered = data[data["item"].isin(places)]
filtered.save(output)
print filtered
def filter_europe(input="geography-first-all-2.pd", output=None):
data = pd.load(input)
places = get_maps()["Europe-country"]
filtered = data[data["item"].isin(places)]
filtered.save(output)
print filtered
def get_all_fund_returns(force_db_read=False, df_file = 'fund_returns_lite_nona_reindex.pandas'):
""" Reads returns for all funds from the database (excludes some things we aren't interested in """
# caching to make things faster most of the time
if not force_db_read and os.path.isfile(df_file):
print "Loading file from cache:",df_file
return pd.load(df_file)
#otherwise read from db
print "Reading fund returns from the database"
try:
con = lite.connect(config['db_path'])
cur = con.cursor()
# run pragmas (case sensivity on)
cur.execute(pragmas)
sql= """select mr.crsp_fundno, caldt, mret from FUND_HDR fhdr, MONTHLY_RETURNS mr
where fhdr.crsp_fundno=mr.crsp_fundno
%s
;""" % common_excludes_data_load
cur.execute(sql)
data = cur.fetchall()
print 'Parsing database results (those pesky date parses take a little while)'
df = pd.DataFrame(list(data),columns=['FundNo', 'StrCalDate', 'Return'])
# parse the date column (string slicing is faster than datetime.strptime)
parsed_dates = df['StrCalDate'].map(lambda x: date(int(str(x)[0:4]),int(str(x)[4:6]),int(str(x)[6:8]) ))
# add back to the dataframe
df['CalDate'] = pd.Series(parsed_dates,index=df.index)
# remove unneeded column
del df['StrCalDate']
# change -99 values to NaN, then drop them
print 'Drop the -99.0 values'
df=df.replace({'Return':-99.0}, value=np.nan)
df=df.dropna(subset=['Return'])
# add one to all the returns per our convention
df['Return']=pd.Series(df['Return'],index=df.index) + 1
print 'Re-index by FundNo and Date'
df.index = [df['FundNo'],pd.DatetimeIndex(df['CalDate'])]
# now that these are indexes, no need to keep the column data
del df['CalDate']
del df['FundNo']
#save the file for next time
print "Saving returns dataframe for next time as",df_file
df.save(df_file)
return df
except lite.Error, e:
print "Error %s:" % e.args[0]
sys.exit(1)
def Improve_plot(filepath):
import os
import pandas as pan
from matplotlib import pyplot as plt
os.chdir(filepath)
df = pan.load('IMPROVE_data_all.pickle')
return df
def load_table():
# Load the table with the cores
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
filename = results_dir + 'tables/multicloud_model_params.pickle'
df = pd.load(filename)
return df
def load(self, name):
'''
Checks for an existing file name and if exists returns the data saved
'''
f = os.path.join(self.cache_dir, name)
if os.access(f, os.F_OK):
return pd.load(f)
else:
return None
def load_quote(currency):
try:
return pd.load(DUKASCOPY_QUOTES_PATH +
'/{}.dat'.format(currency))
except FileNotFoundError as e:
print(e)
return None
except Exception as e:
raise(e)
def load_table():
# Load the table with the cores
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
filename = results_dir + 'tables/multicloud_model_params.pickle'
df = pd.load(filename)
return df
def filter_states(input="geography-first-all-2.pd", output=None):
data = pd.load(input)
places = reduce(list.__add__, get_continents_country_maps().values(), [])
filtered = data[data["item"].isin(places)]
filtered.save(output)
print filtered
def test_wad_dataframe(self):
print("TestParseWad")
try:
path = awac_folder_path + 'test_data.wad'
parse_wad.ParseWad(path)
except WindowsError:
print("Load Wad Files failed")
wad_dataframe = pd.load('test_data_wad_df')
self.assertEqual(len(wad_dataframe),number_of_records)
def Improve_plot(filepath):
import os
import pandas as pan
from matplotlib import pyplot as plt
os.chdir(filepath)
df = pan.load('IMPROVE_data_all.pickle')
return df
def get_r2_bucket_funds(threshold=0.9, force_bucket=False, r2_file='r2_all_funds.pandas', bucket_file='bucketed_r2.pandas'):
""" Creates a dataframe of funds that are highly correlated (by threshold) to each asset class"""
if not force_bucket and os.path.isfile(bucket_file):
print "Loading bucketed R2 file from cache:",bucket_file
return pd.load(bucket_file)
#otherwise create the bucket file
if not os.path.isfile(r2_file):
print "R2 file (%s) does not exist. Run calc_all_fund_r2() first" % r2_file
return False
else:
print "Bucketing by R2"
r2_df = pd.load(r2_file)
# take only the ones over threshold, and drop any rows with no assets highly correlated
bucketed_df=r2_df[r2_df.apply(lambda x: x > threshold, axis=1)].dropna(how='all')
# save for next time
bucketed_df.save(bucket_file)
return bucketed_df
def display_progress():
coherence_data = pd.load('results/coherences_dense.pkl')
rate_data = pd.load('results/rates_dense.pkl')
for distribution in distributions:
for i, algorithm in enumerate(algorithms):
mask = rate_data.loc[distribution].index.get_level_values(0)
avg_rates = []
for ratio in measurement_ratios:
rate = rate_data.loc[distribution].loc[mask==ratio]
rate.index = rate.index.droplevel(0)
#print 'average rate for {}, '.format(ratio) + algorithm + ', ' + distribution
avg_rates.append(np.mean(rate.loc[algorithm]))
plt.plot(index, avg_rates, colors[i], linewidth=2, markersize=7, label=algorithm)
plt.legend(loc=0)
plt.title(distribution, fontsize=16)
plt.xticks(range(5), [0.01,0.03,0.1,0.3,1])
plt.xlabel('measurements / dimension', fontsize=16)
plt.ylabel('log10 rate', fontsize=16)
plt.show()
def test_awac_stats(self):
print("TestAwacStats")
try:
path = awac_folder_path + 'test_data.wad'
parse_wad.ParseWad(path)
awac_stats.process_wave_height(awac_folder_path + 'awac_wave_height_df')
except WindowsError:
print("Load wap Files failed")
wave_height_dataframe = pd.load('awac_stats_30min')
self.assertEqual(len(wave_height_dataframe),number_of_awac_stats)
def get_distance_json(origins=["Paris"], destinations=["Lyon"], key=API_key):
url = "https://maps.googleapis.com/maps/api/distancematrix/json?"
origins = "origins=" + "|".join(origins)
destinations = "destinations=" + "|".join(destinations)
key = "key=" + key
response = requests.get(url + "&" + origins + "&" +
destinations + "&" + key).text
json_data = json.loads(response)
df = pd.load(json_data)
return df
def __init__(self) :
"""This reads two csv files into pandas:
PD_RO: a csv with UIDs, Names, Prices and Modules. It is called RO as it is Read Only. It will never be altered. NOTE: The UIDs are
imported to pandas as the index values.
PD_CL: a csv with UIDs, Names, Contact Details, Status Variables, and Comments. It is called CL as it is a contact list.
This csv will be directly modifiable by the program according to user input. Note: the UIDs are imported to pandas as index values."""
self.PD_RO = pd.read_csv(r"C:\Users\IPAB\Dropbox\MarketingTeamFolder\DataEntry\ReadOnlyFiles\FactoryList\Wave1\FactoryList.csv", index_col = 'UID')
self.PD_CL = pd.read_csv(r"C:\Users\IPAB\Dropbox\MarketingTeamFolder\DataEntry\ContactList\ContactList_Wave1.csv", index_col = 'UID')
self.DF = pd.load(r"C:\Users\IPAB\Dropbox\MarketingTeamFolder\DataEntry\ReadOnlyFiles\DataFrames\Wave1\FollowUp1DataFrame")
def load(store_cache_file=False, cache_file=None):
if not store_cache_file and cache_file:
return pd.load(cache_file)
else:
#TODO load your data into a DataFrame
result = pd.DataFrame() #replace with your data
if store_cache_file and cache_file:
result.to_csv(cache_file)
return result
def load_data(path, smoothstr):
return (
pd.load(path + "/expmean" + smoothstr + ".df"),
pd.load(path + "/ctrlmean" + smoothstr + ".df"),
pd.load(path + "/expstd" + smoothstr + ".df"),
pd.load(path + "/ctrlstd" + smoothstr + ".df"),
pd.load(path + "/expn" + smoothstr + ".df"),
pd.load(path + "/ctrln" + smoothstr + ".df"),
pd.load(path + "/df2" + smoothstr + ".df"),
)
def get_r2_bucket_funds(threshold=0.9,
force_bucket=False,
r2_file='r2_all_funds.pandas',
bucket_file='bucketed_r2.pandas'):
""" Creates a dataframe of funds that are highly correlated (by threshold) to each asset class"""
if not force_bucket and os.path.isfile(bucket_file):
print "Loading bucketed R2 file from cache:", bucket_file
return pd.load(bucket_file)
#otherwise create the bucket file
if not os.path.isfile(r2_file):
print "R2 file (%s) does not exist. Run calc_all_fund_r2() first" % r2_file
return False
else:
print "Bucketing by R2"
r2_df = pd.load(r2_file)
# take only the ones over threshold, and drop any rows with no assets highly correlated
bucketed_df = r2_df[r2_df.apply(lambda x: x > threshold,
axis=1)].dropna(how='all')
# save for next time
bucketed_df.save(bucket_file)
return bucketed_df
def main(outfile,
infile='sources.pickle',
cuts='(sources.ts>10)*(sources.a<0.25)'):
assert os.path.exists(infile), 'Input file "%s" not found' % infile
sources = pd.load(infile)
print 'Loaded DataTable file %s' % infile
selected = sources[eval(cuts)]
print 'applied cuts %s: %d -> %d sources' % (cuts, len(sources),
len(selected))
t = MakeCat(selected)
if outfile is None:
outfile = '_'.join(os.path.abspath('.').split('/')[-2:]) + '.fits'
# for example, 'P202_uw10.fits'
t(outfile)
def load_cold_cores():
# summary of cold clump data
# http://wiki.cosmos.esa.int/planckpla2015/index.php/Catalogues#Individual_catalogues
table_dir = '/d/bip3/ezbc/multicloud/data/cold_clumps/'
df_dir = '/d/bip3/ezbc/multicloud/data/python_output/tables/'
filename = table_dir + 'HFI_PCCS_GCC_R2.02.fits'
if 0:
print('\nAnalyzing table...')
cc_hdu = fits.open(filename)
cc_data = cc_hdu[1].data
# get the region vertices
regions = load_regions()
df = dict()
df['Glon'] = []
df['Glat'] = []
df['ra'] = []
df['dec'] = []
df['Region'] = []
df['SNR'] = []
for i in xrange(len(cc_data)):
#if myg.point_in_polygon(ra, region_vertices):
ra = cc_data[i][3]
dec = cc_data[i][4]
#if ra < 80 and ra > 40 and dec < 45 and dec > 15:
region_check = check_region((ra, dec), regions)
if region_check is not None:
df['Glon'].append(cc_data.field('GLON')[i])
df['Glat'].append(cc_data.field('GLAT')[i])
df['ra'].append(cc_data.field('RA')[i])
df['dec'].append(cc_data.field('DEC')[i])
df['SNR'].append(cc_data.field('SNR')[i])
df['Region'].append(region_check)
df = pd.DataFrame(df)
df.save(df_dir + 'multicloud_cold_clumps.pickle')
else:
df = pd.load(df_dir + 'multicloud_cold_clumps.pickle')
print('\nFinished loading...')
return df
def main(args):
df = pd.load(args.df)
svc_seed = int(time.time() * 1000)
estimators = [('reduce_dim', KernelPCA(kernel='linear')),
('svm', LinearSVC(random_state=svc_seed))]
clf = Pipeline(estimators)
equiv_sets = None
params = dict(reduce_dim__n_components=[2, 3, 5, 8, 10, 25, 50, 100],
svm__C=[0.25, 0.5, 1, 2, 4, 8, 16])
if args.twoclass:
params = dict(reduce_dim__n_components=[
2, 3, 5, 8, 10, 25, 40, 45, 50, 55, 60, 75, 100
])
equiv_sets = [['external plexiform', 'glomerular cell layer'],
['internal plexiform', 'mitral cell layer']]
grid_search = GridSearchCV(clf, param_grid=params, cv=3)
best = 0.0
while True:
random_seed = int(time.time() * 1000)
random.seed(random_seed)
df_l, _, df_t = split_df(df, 0.33)
y_l = integer_labels(df_l, equiv_sets)
try:
grid_search.fit(df_l, y_l)
except ValueError:
# Most likely wrong number of classes in cross validation set
continue
y_t = integer_labels(df_t, equiv_sets)
c = grid_search.predict(df_t)
score, c = compare_clusters(c, y_t)
if score > best:
best = score
print('{} {} {}'.format(svc_seed, random_seed, best))
with open(args.out, 'a') as fh:
fh.write('{} {} {}\n'.format(svc_seed, random_seed, best))
def setup(self, othermodel='../P202_5years/uw700', **kw):
super(UWsourceComparison,self).setup()
self.plotfolder = 'comparison_%s' % othermodel.split('/')[-1]
otherfilename = '../%s/sources.pickle' %othermodel
self.othermodel=othermodel
assert os.path.exists(otherfilename), 'File %s not found' % otherfilename
print 'loading %s' % otherfilename
odf = pd.load(otherfilename)
self.odf = odf[odf.ts>10]
self.df = self.df[self.df.ts>10]
self.df['pindex_old']=self.odf.pindex
self.df['ts_old'] = self.odf.ts
self.df['eflux_old']=self.odf.eflux
self.df['a_old'] = self.odf.a
self.df['skydir_old'] = self.odf.skydir
self.df['sedrec_old'] = self.odf.sedrec
def main(args):
df = pd.load(args.df)
y = integer_labels(df)
pca = KernelPCA(None, kernel=args.kernel)
pca.fit(df)
X = pca.transform(df)
nonzero_components = X.shape[1]
seed = int(time.time() * 1000)
gmm = GMM(4, n_init=10, random_state=seed)
gmm.fit(X)
c = gmm.predict(X)
score, _ = compare_clusters(c, y)
best = score
with open(args.out, 'w') as fh:
fh.write('{} {} {} {}\n'.format(args.kernel, nonzero_components, seed,
best))
n_comps = range(
2, 16) + [int(i) for i in np.linspace(16, nonzero_components, 20)]
for n in n_comps:
pca = KernelPCA(n, kernel=args.kernel)
pca.fit(df)
X = pca.transform(df)
for i in range(128):
seed = int(time.time() * 1000)
gmm = GMM(4, random_state=seed)
gmm.fit(df)
c = gmm.predict(df)
score, _ = compare_clusters(c, y)
if score > best:
best = score
with open(args.out, 'a'):
fh.write('{} {} {} {}\n'.format(args.kernel, n, seed,
best))
def traceproc(aerofilt_dir):
import hysplit_tools as tools
import os, sys
import numpy as np
import pandas as pan
startdir = os.getcwd()
topdir = aerofilt_dir
os.chdir(topdir)
data_files = os.listdir(os.getcwd())
d_mean = []
d_std = []
t_mean = []
t_std = []
endpos_mean = []
endpos_std = []
start_time = []
station = []
#run through all location folders
for f in data_files:
if os.path.isdir(f):
os.chdir(f)
tracefile = f + 'traceback'
#open traceback file
trace_df = pan.load(tracefile)
#create a separate dict of lists for each day and put those into a
#list called dictlist
dates = trace_df.index()
keys = trace_df.columns()
by = lambda x: lambda y: getattr(y, x)
trace_mean = trace_df.groupby([by('month'), by('day')]).mean()
trace_std = trace_df.groupby([by('month'), by('day')]).std()
pan.save(df_out, 'Hyproc.pickle')
os.chdir(startdir)
