code stringlengths 3 6.57k |
|---|
df_.drop(["EP", "Num", "Ext"], axis=1) |
apply(lambda x: prepare_name(x, True) |
apply(lambda x: h.handle(x) |
unnest_attr(patent_dict: dict, publication_number: str) |
list(filter(lambda x: x not in NESTED_ATTR, patent_dict.keys() |
validate.single_attr(val, attr, publication_number) |
serialize_patent_df(patent_df: pd.DataFrame) |
patent_df.drop(["publication_number", "publication_date"], axis=1) |
groupby("attr") |
aggregate(list) |
T.to_dict() |
unnest_attr(out, publication_number) |
out.update({"publication_number": publication_number}) |
out.update({"publication_date": publication_date}) |
format_patent_df(data, prepare_names, handle_html) |
serialize_patent_df(out) |
callbackRaw(imu_in) |
if (lastClean != 0 and lastClean > rospy.Time.now() |
rospy.Duration(1) |
if (lastPub == 0 or imu_in.header.stamp > lastPub) |
pub.publish(imu_in) |
callbackData(imu_in) |
if (lastPub == 0 or imu_in.header.stamp > lastPub) |
pub.publish(imu_in) |
filter_imu() |
rospy.init_node('clean_imu_data', anonymous=True) |
rospy.Subscriber('/mavros/imu/data_raw', Imu, callbackRaw) |
rospy.Subscriber('/mavros/imu/data', Imu, callbackData) |
rospy.spin() |
rospy.sleep(1) |
rospy.Publisher('filtered_imu', Imu, queue_size=10) |
filter_imu() |
tf2_ros.Buffer(rospy.Duration(10.0) |
tf2_ros.TransformListener(tf_buffer) |
find_peaks_original(x, scale=None, debug=False) |
of (2 * scale + 1) |
detrend(x) |
len(x) |
min(scale, L) |
np.zeros((L, N) |
np.arange(1, L) |
LSM.sum(axis=1) |
np.argmax(G) |
np.min(LSM[0:l_scale, :], axis=0) |
np.flatnonzero(pks_logical) |
find_peaks(x, scale=None, debug=False) |
of (2 * scale + 1) |
detrend(x) |
len(x) |
min(scale, L) |
np.ones((L, N) |
np.arange(1, L + 1) |
LSM.sum(axis=1) |
np.argmax(G) |
np.min(LSM[0:l_scale, :], axis=0) |
np.flatnonzero(pks_logical) |
find_peaks_adaptive(x, window=None, debug=False) |
detrend(x) |
len(x) |
np.ones((L, N) |
np.arange(1, L + 1) |
uniform_filter1d(LSM * window, window, axis=1, mode='nearest') |
np.arange(L, 0, -1) |
normalization.reshape(-1, 1) |
ass_LSM.argmax(axis=0) |
adaptive_scale.max() |
np.indices(LSM_reduced.shape) |
np.min(LSM_reduced, axis=0) |
np.flatnonzero(pks_logical) |
imp.load_source('GenMeteoFuncs', '/nfs/see-fs-01_users/eepdw/python_scripts/modules/GeneralMeteoFunctions.py') |
imp.load_source('SoundingRoutines', '/nfs/see-fs-01_users/eepdw/python_scripts/Tephigram/Sounding_Routines.py') |
imp.load_source('GeogFuncs', '/nfs/see-fs-01_users/eepdw/python_scripts/modules/GeogFunctions.py') |
datetime.datetime(2011,5,1,0,0,0) |
datetime.datetime(2011,10,1,0,0,0) |
relativedelta(weeks=+1) |
variable_name_index_match(variable, variable_list) |
variable_list.iteritems() |
key.startswith('%s' % variable) |
variable_cat(var_index, station_list_cs) |
date_min.strftime('%Y%m%d') |
date_max.strftime('%Y%m%d') |
np.empty(load_file['min_max_date_bin'].shape) |
StationInfoSearch(stat) |
CalculateDistanceFromFirstStation(stat, first_station_lon, first_station_lat, station_lat, station_lon) |
var_cat.append(load_file['date_bin_mean_all_dates_one_station'][:,var_index,:]) |
distances.append(dist_from_first_station) |
pdb.set_trace() |
any() |
np.ma.masked_outside(load_file['min_max_date_bin'], date_min, date_max ) |
np.where((load_file['min_max_date_bin']>date_min) |
np.array(var_cat) |
np.array(var_cat) |
np.array(distances, dtype=float) |
station_name_plot(station_list_cs, first_station, yi) |
StationInfoSearch(first_station) |
StationInfoSearch(stat) |
CalculateDistanceFromFirstStation(stat, first_station_lon, first_station_lat, station_lat, station_lon) |
plt.axvline(x=dist_from_first_station, ymin=0, ymax=1, label=station_title, color='k') |
plt.text(dist_from_first_station+0.1,max(yi) |
grid_data_cs(pressure, distance, param) |
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