index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
73,762 | DeRaafMedia/ProjectIRCInteractivity | refs/heads/master | /skills/template_serial_device.py | import sys
import serial
def main(arg_1, arg_2, arg_3, arg_4):
serial_port = str(arg_1)
baud_rate = int(arg_2)
time_out = int(arg_3)
parameter = int(arg_4)
device = serial.Serial(serial_port, baud_rate, timeout=time_out)
# Function here
pass
if __name__ == "__main__":
main(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
pass
| {"/Arduino.py": ["/SerialPort.py"]} |
73,763 | DeRaafMedia/ProjectIRCInteractivity | refs/heads/master | /Utilities.py | __author__ = 'DeRaaf'
# TODO Clean up comments. Fix bugs. On going project!
import os
from os import system
import sys
import ConfigParser
import threading
import csv
import time
class Utilities (object):
def __init__(self):
self.preference_parser = ConfigParser.RawConfigParser()
self.thread = threading.Thread()
self.preference_file = 'pref/preferences.txt'
self.initiate_preference()
self.chat_log_enabled = self.read_preference('Log Settings', 'chat')
self.voice_enabled = self.read_preference('Speak', 'voice_enabled')
self.chat_voice_enabled = self.read_preference('Speak', 'chat_voice_enabled')
self.announcement_voice_enabled = self.read_preference('Speak', 'announcement_voice_enabled')
self.voice = self.read_preference('Voices', 'voice')
self.chat_voice = self.read_preference('Voices', 'chat_voice')
self.announcement_voice = self.read_preference('Voices', 'announcement_voice')
self.chat_directory = 'logs/chat/'
self.timestamp = time.strftime('%m%d%Y%H%M')
self.chat_log_file = ''
def __str__(self):
return '\n\nCallable methods:\n\n' \
'.initiate_preferences : Creates a preference file\n' \
'.read_preferences : Reads from preferences file\n' \
'.write_preferences : Write to preferences file' \
'.get_preference_value : Get the value for a variable' \
'' \
'\n\n'.format()
def __getattr__(self):
return '{0}'.format('Not Found')
def initiate_preference(self):
"""
Creates a preference file (i.e. .initiate("pref/preferences.txt"))
:return:
"""
if not os.path.exists(self.preference_file):
temp_file = open(self.preference_file, 'w+')
self.preference_parser.add_section('Speak')
self.preference_parser.set('Speak', 'voice_enabled', 'yes')
self.preference_parser.set('Speak', 'chat_voice_enabled', 'yes')
self.preference_parser.set('Speak', 'announcement_voice_enabled', 'yes')
self.preference_parser.add_section('Voices')
self.preference_parser.set('Voices', 'voice', 'Zarvox')
self.preference_parser.set('Voices', 'chat_voice', 'Alex')
self.preference_parser.set('Voices', 'announcement_voice', 'Whisper')
self.preference_parser.add_section('Log Settings')
self.preference_parser.set('Log Settings', 'chat', 'yes')
self.preference_parser.write(open(self.preference_file, 'w'))
temp_file.close()
else:
pass
def read_preference(self, session, key):
"""
preference_file -> file to read/write from/to (i.e 'pref/preferences.txt')
section -> Which section of preferences (i.e 'Speech')
key -> Which key of preferences (i.e 'speech_enabled')
Reads from preferences file (i.e. .read("pref/preferences.txt", "section", "key"))
:param session:
:param key:
:return:
"""
temp_file = open(self.preference_file, 'r')
self.preference_parser.readfp(temp_file)
temp_value = self.preference_parser.get(session, key)
temp_file.close()
return temp_value
def write_preference(self, section, key, value):
"""
preference_file -> file to read/write from/to (i.e 'pref/preferences.txt')
section -> Which section of preferences (i.e 'Speech')
key -> Which key of preferences (i.e 'speech_enabled')
value -> The value to be writen (i.e 'yes')
Write to preferences file (i.e .write("pref/preferences.txt", "section", "key", "value"))
:param session:
:param key:
:param value:
:return:
"""
temp_file = open(self.preference_file, 'r')
self.preference_parser.readfp(temp_file)
self.preference_parser.set(section, key, value)
self.preference_parser.write(open(self.preference_file, 'w'))
temp_file.close()
def get_preference_value(self, preference):
"""
preference -> Value to return
These can be returned
'chat_log_enabled'
'voice_enabled'
'chat_voice_enabled'
'announcement_voice_enabled'
:param preference:
:return:
"""
if preference == 'chat_log_enabled':
return self.chat_log_enabled
if preference == 'voice_enabled':
return self.voice_enabled
if preference == 'chat_voice_enabled':
return self.chat_voice_enabled
if preference == 'announcement_voice_enabled':
return self.announcement_voice_enabled
if preference == 'voice':
return self.voice
if preference == 'chat_voice':
return self.chat_voice
if preference == 'announcement_voice':
return self.announcement_voice
if preference == 'chat_log_file':
return self.chat_log_file
def create_chat_log(self, irc_bot_name):
"""
irc_bot_name -> The name of the IRCBot
This creates a new chat log file for every session started
:param irc_bot_name:
:return:
"""
if self.chat_log_enabled == 'yes':
if not os.path.exists(self.chat_directory):
os.makedirs(self.chat_directory, mode=0755)
self.chat_log_file = str(irc_bot_name + '.' + self.timestamp+'.txt')
else:
pass
def write_chat_log(self, sentence):
"""
sentence -> String send from the IRCBot to log
Write to the chat log
:param sentence:
:return:
"""
if self.chat_log_enabled == 'yes':
log_file = open((self.chat_directory + self.chat_log_file), "a") # chat log file
log_file.write(str(sentence[0] + ' : ' + sentence[1]))
log_file.close()
else:
pass
def new_thread(self,
as_daemon,
function,
*parameters):
"""
as_daemon -> Yes if a function needs to be a Daemon process or not
function -> The name of the function passed through from Brain.csv
parameters -> The parameters the function needs
:param as_daemon:
:param function:
:param parameters:
"""
if as_daemon == 'yes':
self.thread.daemonSet = True
self.thread.__init__(target=function, name=str(function), args=parameters)
self.thread.start()
# self.thread.join()
else:
self.thread.__init__(target=function, name=str(function), args=parameters)
self.thread.start()
# self.thread.join()
def speak(self, voice, sentence):
"""
voice -> Name of the Mac OS X voice to be used (i.e Alex)
:param voice:
:param sentence:
"""
if 'darwin' in sys.platform:
if voice:
system('say -v ' + voice + ' ' + sentence)
else:
system('say ' + sentence)
else:
pass
def parse_irc_chat(self, sentence):
"""
sentence -> Raw IRC strings
type_of_return -> 'all' give back a tuple with all eh IRC info. text_only gives back senteces
Takes in the raw string from the IRC chat and converts it to something more manageable
:param sentence:
:return:
"""
irc_prefix = ''
irc_trailing = ''
if sentence.startswith(':'):
irc_prefix, sentence = sentence[1:].split(' ', 1)
if ' :' in sentence:
sentence, irc_trailing = sentence.split(' :', 1)
irc_arguments = sentence.split()
return irc_prefix, irc_arguments.pop(0), irc_arguments, irc_trailing
def check_conversation(self, sentence, irc_bot_name):
"""
sentence -> Takes parsed (or raw) IRC communication and check it against the Brains.csv file
irc_bot_name -> Helps if you want multiple IRCBots Walking around
Check a cerain string against the Brin.csv
:param sentence:
:param irc_bot_name:
:return:
"""
where_is_my_brain = os.path.join((os.getcwd()), 'brains', irc_bot_name.replace(' ', '_'), 'Brain.csv')
with open(where_is_my_brain, 'rb') as brain:
dialect = csv.Sniffer().sniff(brain.read(1024),
delimiters=';,')
brain.seek(0)
deep_thoughts = csv.reader(brain,
dialect)
for thought in deep_thoughts:
if sentence.lower().find(thought[0]) != -1:
return thought
def set_toggle_state(self, sentence, irc_bot_nick, check):
"""
sentence -> The sentence to check
irc_the_nick -> For which IRCBot is this test
check -> What to test takes in a number (i.e 1)
check [0] -> Chat log
check [1] -> IRCBot speech
check [2] -> Chat room speech
check [3] -> Nick announcement
:param sentence:
:param irc_bot_nick:
:param check:
"""
checks_array = [['.toggleChatLog',
self.chat_log_enabled,
'self.chat_log_enabled',
'Log Settings',
'chat'],
['.toggleVoice',
self.voice_enabled,
'self.voice_enabled',
'Speak',
'voice_enabled'],
['.toggleChatVoice',
self.chat_voice_enabled,
'self.chat_voice_enabled',
'Speak',
'chat_voice_enabled'],
['.toggleNickVoice',
self.announcement_voice_enabled,
'self.announcement_voice_enabled',
'Speak',
'announcement_voice_enabled']]
if sentence.find(irc_bot_nick + checks_array[check][0]) != -1:
if checks_array[check][1] == 'yes':
execute = checks_array[check][2] + ' = "no"'
exec execute
self.write_preference(checks_array[check][3], checks_array[check][4], 'no')
return True
else:
execute = checks_array[check][2] + ' = "yes"'
exec execute
self.write_preference(checks_array[check][3], checks_array[check][4], 'yes')
return True
else:
return False
def load_skills_init(self, path_to_skills):
"""
path_to_skills -> Path to the skills directory (i.e 'skills/')
This function takes in al of the skill script files and puts them into the __init__.py file so the directory
(and all skills in it) can be imported for use.
:param path_to_skills:
"""
files = os.listdir(path_to_skills)
skill_scripts = []
for i in range(len(files)):
name = files[i].split('.')
if len(name) > 1:
if name[1] == 'py' and name[0] != '__init__':
name = name[0]
skill_scripts.append(name)
init_file = open(path_to_skills+'__init__.py', 'w')
to_write = '__all__ = '+str(skill_scripts)
init_file.write(to_write)
init_file.close() | {"/Arduino.py": ["/SerialPort.py"]} |
73,764 | DeRaafMedia/ProjectIRCInteractivity | refs/heads/master | /skills/test_act_function.py | import sys
import serial
from time import sleep
def test_act_function(arg_1, arg_2, arg_3, arg_4):
serial_port = str(arg_1)
baud_rate = int(arg_2)
time_out = int(arg_3)
parameter = int(arg_4)
device = serial.Serial(serial_port, baud_rate, timeout=time_out)
x = 0
for i in range(0, parameter):
while x < 255:
device.write('1/1/9/' + str(x) + '/')
sleep(0.000005)
x += 1
sleep(0.000005)
sleep(0.000005)
while x > 0:
device.write('1/1/9/' + str(x) + '/')
sleep(0.000005)
x -= 1
sleep(0.000005)
sleep(0.000005)
device.write('1/1/9/0/')
device.close()
if __name__ == "test_act_function":
test_act_function(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]) | {"/Arduino.py": ["/SerialPort.py"]} |
73,765 | DeRaafMedia/ProjectIRCInteractivity | refs/heads/master | /skills/test_feel_function.py | import sys
import serial
from time import sleep
def test_feel(arg_1, arg_2, arg_3, arg_4):
serial_port = str(arg_1)
baud_rate = int(arg_2)
time_out = int(arg_3)
parameter = int(arg_4)
device = serial.Serial(serial_port, baud_rate, timeout=time_out)
for i in range(0, 10000):
device.write('1/1/11/0/')
device.write('1/2/22/1/')
device.write('1/2/24/2/')
device.write('2/2/23/')
x = int(device.readline().strip())
print(x)
if x == 1:
device.write('1/2/9/2/')
else:
device.write('1/2/9/1/')
device.write('1/1/9/0/')
device.write('1/2/22/1/')
device.write('1/1/24/1/')
device.close()
if __name__ == "__test_feel__":
test_feel(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]) | {"/Arduino.py": ["/SerialPort.py"]} |
73,767 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/set_groups_ad2cp.py | from enum import Enum, auto, unique
from importlib import resources
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
import xarray as xr
import yaml
from .. import convert
from ..utils.coding import set_time_encodings
from .parse_ad2cp import DataType, Dimension, Field, HeaderOrDataRecordFormats
from .set_groups_base import SetGroupsBase
AHRS_COORDS: Dict[Dimension, np.ndarray] = {
Dimension.MIJ: np.array(["11", "12", "13", "21", "22", "23", "31", "32", "33"]),
Dimension.WXYZ: np.array(["w", "x", "y", "z"]),
Dimension.XYZ: np.array(["x", "y", "z"]),
}
@unique
class BeamGroup(Enum):
AVERAGE = auto()
BURST = auto()
ECHOSOUNDER = auto()
ECHOSOUNDER_RAW = auto()
class SetGroupsAd2cp(SetGroupsBase):
"""Class for saving groups to netcdf or zarr from Ad2cp data files."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: bug: 0 if not exist in first string packet
# resulting in index error in setting ds["pulse_compressed"]
self.pulse_compressed = self.parser_obj.get_pulse_compressed()
self._make_time_coords()
with resources.open_text(convert, "ad2cp_fields.yaml") as f:
self.field_attrs: Dict[str, Dict[str, Dict[str, str]]] = yaml.safe_load(f) # type: ignore # noqa
def _make_time_coords(self):
timestamps = []
times_idx = {
Dimension.PING_TIME_AVERAGE: [],
Dimension.PING_TIME_BURST: [],
Dimension.PING_TIME_ECHOSOUNDER: [],
Dimension.PING_TIME_ECHOSOUNDER_RAW: [],
Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT: [],
}
for packet in self.parser_obj.packets:
if not packet.has_timestamp():
continue
timestamps.append(packet.timestamp)
i = len(timestamps) - 1
if packet.is_average():
times_idx[Dimension.PING_TIME_AVERAGE].append(i)
elif packet.is_burst():
times_idx[Dimension.PING_TIME_BURST].append(i)
elif packet.is_echosounder():
times_idx[Dimension.PING_TIME_ECHOSOUNDER].append(i)
elif packet.is_echosounder_raw():
times_idx[Dimension.PING_TIME_ECHOSOUNDER_RAW].append(i)
elif packet.is_echosounder_raw_transmit():
times_idx[Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT].append(i)
self.times_idx = {
time_dim: np.array(time_values, dtype="u8")
for time_dim, time_values in times_idx.items()
}
self.timestamps = np.array(timestamps)
_, unique_ping_time_idx = np.unique(self.timestamps, return_index=True)
self.times_idx[Dimension.PING_TIME] = unique_ping_time_idx
def _make_dataset(self, var_names: Dict[str, str]) -> xr.Dataset:
"""
Constructs a dataset of the given variables using parser_obj data
var_names maps parser_obj field names to output dataset variable names
"""
# {field_name: [field_value]}
# [field_value] lines up with time_dim
fields: Dict[str, List[np.ndarray]] = {field_name: [] for field_name in var_names.keys()}
# {field_name: [Dimension]}
dims: Dict[str, List[Dimension]] = dict()
# {field_name: field dtype}
dtypes: Dict[str, np.dtype] = dict()
# {field_name: attrs}
attrs: Dict[str, Dict[str, str]] = dict()
# {field_name: [idx of padding]}
pad_idx: Dict[str, List[int]] = {field_name: [] for field_name in var_names.keys()}
# {field_name: field exists}
field_exists: Dict[str, bool] = {field_name: False for field_name in var_names.keys()}
beam_coords: Optional[np.ndarray] = None
# separate by time dim
for packet in self.parser_obj.packets:
if not packet.has_timestamp():
continue
if "beams" in packet.data:
if beam_coords is None:
beam_coords = packet.data["beams"]
else:
beam_coords = max(beam_coords, packet.data["beams"], key=lambda x: len(x))
data_record_format = HeaderOrDataRecordFormats.data_record_format(
packet.data_record_type
)
for field_name in var_names.keys():
field = data_record_format.get_field(field_name)
if field is None:
field_dimensions = Field.default_dimensions()
# can't store in dims yet because there might be another data record format
# which does have this field
if field_name not in attrs:
if field_name in self.field_attrs["POSTPROCESSED"]:
attrs[field_name] = self.field_attrs["POSTPROCESSED"][field_name]
else:
field_dimensions = field.dimensions(packet.data_record_type)
if field_name not in dims:
dims[field_name] = field_dimensions
if field_name not in dtypes:
field_entry_size_bytes = field.field_entry_size_bytes
if callable(field_entry_size_bytes):
field_entry_size_bytes = field_entry_size_bytes(packet)
dtypes[field_name] = field.field_entry_data_type.dtype(
field_entry_size_bytes
)
if field_name not in attrs:
attrs[field_name] = self.field_attrs[data_record_format.name][field_name]
if field_name in packet.data: # field is in this packet
fields[field_name].append(packet.data[field_name])
field_exists[field_name] = True
else: # field is not in this packet
# pad the list of field values with an empty array so that
# the time dimension still lines up with the field values
fields[field_name].append(np.array(0))
pad_idx[field_name].append(len(fields[field_name]) - 1)
for field_name in fields.keys():
# add dimensions to dims if they were not found
# (the desired fields did not exist in any of the packet's data records
# because they are in a different packet OR it is a field created by echopype
# from a bitfield, etc.)
if field_name not in dims:
dims[field_name] = Field.default_dimensions()
# add dtypes to dtypes if they were not found
# (the desired fields did not exist in any of the packet's data records
# because they are in a different packet OR it is a field created by echopype
# from a bitfield, etc.)
if field_name not in dtypes:
dtypes[field_name] = DataType.default_dtype()
# replace padding with correct shaped padding
# (earlier we padded along the time dimension but we didn't necessarily know the shape
# of the padding itself)
for field_name, pad_idxs in pad_idx.items():
for i in pad_idxs:
fields[field_name][i] = np.zeros(
np.ones(len(dims[field_name]) - 1, dtype="u1"), # type: ignore
dtype=dtypes[field_name],
)
# {field_name: field_value}
# field_value is now combined along time_dim
combined_fields: Dict[str, np.ndarray] = dict()
# pad to max shape and stack
for field_name, field_values in fields.items():
if field_exists[field_name]:
if len(dims[field_name]) > 1:
shapes = [field_value.shape for field_value in field_values]
max_shape = np.amax(
np.stack(shapes),
axis=0,
)
field_values = [
np.pad(
field_value,
tuple(
(0, max_axis_len - field_value.shape[i])
for i, max_axis_len in enumerate(max_shape) # type: ignore
),
)
for field_value in field_values
]
field_values = np.stack(field_values)
combined_fields[field_name] = field_values
# slice fields to time_dim
for field_name, field_value in combined_fields.items():
combined_fields[field_name] = field_value[self.times_idx[dims[field_name][0]]]
# make ds
used_dims: Set[Dimension] = {
dim
for field_name, dims_list in dims.items()
for dim in dims_list
if field_exists[field_name]
}
data_vars: Dict[
str,
Union[Tuple[List[str], np.ndarray, Dict[str, str]], Tuple[Tuple[()], None]],
] = {
var_name: (
[dim.dimension_name() for dim in dims[field_name]],
combined_fields[field_name],
attrs.get(field_name, {}),
)
if field_exists[field_name]
else ((), None)
for field_name, var_name in var_names.items()
} # type: ignore
coords: Dict[str, np.ndarray] = dict()
for time_dim, time_idxs in self.times_idx.items():
if time_dim in used_dims:
coords[time_dim.dimension_name()] = self.timestamps[time_idxs]
for ahrs_dim, ahrs_coords in AHRS_COORDS.items():
if ahrs_dim in used_dims:
coords[ahrs_dim.dimension_name()] = ahrs_coords
if Dimension.BEAM in used_dims and beam_coords is not None:
coords[Dimension.BEAM.dimension_name()] = beam_coords
ds = xr.Dataset(data_vars=data_vars, coords=coords)
# make arange coords for the remaining dims
non_coord_dims = {dim.dimension_name() for dim in used_dims} - set(ds.coords.keys())
ds = ds.assign_coords({dim: np.arange(ds.dims[dim]) for dim in non_coord_dims})
return ds
def set_env(self) -> xr.Dataset:
ds = self._make_dataset(
{
"speed_of_sound": "sound_speed_indicative",
"temperature": "temperature",
"pressure": "pressure",
}
)
return set_time_encodings(ds)
def set_platform(self) -> xr.Dataset:
ds = self._make_dataset(
{
"heading": "heading",
"pitch": "pitch",
"roll": "roll",
}
)
return set_time_encodings(ds)
def set_beam(self) -> List[xr.Dataset]:
# TODO: should we divide beam into burst/average (e.g., beam_burst, beam_average)
# like was done for range_bin (we have range_bin_burst, range_bin_average,
# and range_bin_echosounder)?
beam_groups = []
self._beamgroups = []
beam_groups_exist = set()
for packet in self.parser_obj.packets:
if packet.is_average():
beam_groups_exist.add(BeamGroup.AVERAGE)
elif packet.is_burst():
beam_groups_exist.add(BeamGroup.BURST)
elif packet.is_echosounder():
beam_groups_exist.add(BeamGroup.ECHOSOUNDER)
elif packet.is_echosounder_raw():
beam_groups_exist.add(BeamGroup.ECHOSOUNDER_RAW)
if len(beam_groups_exist) == len(BeamGroup):
break
# average
if BeamGroup.AVERAGE in beam_groups_exist:
beam_groups.append(
self._make_dataset(
{
"num_beams": "number_of_beams",
"coordinate_system": "coordinate_system",
"num_cells": "number_of_cells",
"blanking": "blanking",
"cell_size": "cell_size",
"velocity_range": "velocity_range",
"echosounder_frequency": "echosounder_frequency",
"ambiguity_velocity": "ambiguity_velocity",
"dataset_description": "data_set_description",
"transmit_energy": "transmit_energy",
"velocity_scaling": "velocity_scaling",
"velocity_data_average": "velocity",
"amplitude_data_average": "amplitude",
"correlation_data_average": "correlation",
}
)
)
self._beamgroups.append(
{
"name": f"Beam_group{len(self._beamgroups) + 1}",
"descr": (
"contains echo intensity, velocity and correlation data "
"as well as other configuration parameters from the Average mode."
),
}
)
# burst
if BeamGroup.BURST in beam_groups_exist:
beam_groups.append(
self._make_dataset(
{
"num_beams": "number_of_beams",
"coordinate_system": "coordinate_system",
"num_cells": "number_of_cells",
"blanking": "blanking",
"cell_size": "cell_size",
"velocity_range": "velocity_range",
"echosounder_frequency": "echosounder_frequency",
"ambiguity_velocity": "ambiguity_velocity",
"dataset_description": "data_set_description",
"transmit_energy": "transmit_energy",
"velocity_scaling": "velocity_scaling",
"velocity_data_burst": "velocity",
"amplitude_data_burst": "amplitude",
"correlation_data_burst": "correlation",
}
)
)
self._beamgroups.append(
{
"name": f"Beam_group{len(self._beamgroups) + 1}",
"descr": (
"contains echo intensity, velocity and correlation data "
"as well as other configuration parameters from the Burst mode."
),
}
)
# echosounder
if BeamGroup.ECHOSOUNDER in beam_groups_exist:
ds = self._make_dataset(
{
"num_beams": "number_of_beams",
"coordinate_system": "coordinate_system",
"num_cells": "number_of_cells",
"blanking": "blanking",
"cell_size": "cell_size",
"velocity_range": "velocity_range",
"echosounder_frequency": "echosounder_frequency",
"ambiguity_velocity": "ambiguity_velocity",
"dataset_description": "data_set_description",
"transmit_energy": "transmit_energy",
"velocity_scaling": "velocity_scaling",
"correlation_data_echosounder": "correlation",
"echosounder_data": "amplitude",
}
)
ds = ds.assign_coords({"echogram": np.arange(3)})
pulse_compressed = np.zeros(3)
# TODO: bug: if self.pulse_compress=0 this will set the last index to 1
pulse_compressed[self.pulse_compressed - 1] = 1
ds["pulse_compressed"] = (("echogram",), pulse_compressed)
beam_groups.append(ds)
self._beamgroups.append(
{
"name": f"Beam_group{len(self._beamgroups) + 1}",
"descr": (
"contains backscatter echo intensity and other configuration "
"parameters from the Echosounder mode. "
"Data can be pulse compressed or raw intensity."
),
}
)
# echosounder raw
if BeamGroup.ECHOSOUNDER_RAW in beam_groups_exist:
beam_groups.append(
self._make_dataset(
{
"num_beams": "number_of_beams",
"coordinate_system": "coordinate_system",
"num_cells": "number_of_cells",
"blanking": "blanking",
"cell_size": "cell_size",
"velocity_range": "velocity_range",
"echosounder_frequency": "echosounder_frequency",
"ambiguity_velocity": "ambiguity_velocity",
"dataset_description": "data_set_description",
"transmit_energy": "transmit_energy",
"velocity_scaling": "velocity_scaling",
"num_complex_samples": "num_complex_samples",
"ind_start_samples": "ind_start_samples",
"freq_raw_sample_data": "freq_raw_sample_data",
"echosounder_raw_samples_i": "backscatter_r",
"echosounder_raw_samples_q": "backscatter_i",
"echosounder_raw_transmit_samples_i": "transmit_pulse_r",
"echosounder_raw_transmit_samples_q": "transmit_pulse_i",
}
)
)
self._beamgroups.append(
{
"name": f"Beam_group{len(self._beamgroups) + 1}",
"descr": (
"contains complex backscatter raw samples and other configuration "
"parameters from the Echosounder mode, "
"including complex data from the transmit pulse."
),
}
)
# FIXME: this is a hack because the current file saving
# mechanism requires that the beam group have ping_time as a dimension,
# but ping_time might not be a dimension if the dataset is completely
# empty
for i, ds in enumerate(beam_groups):
if "ping_time" not in ds.dims:
beam_groups[i] = ds.expand_dims(dim="ping_time")
# remove time1 from beam groups
for i, ds in enumerate(beam_groups):
beam_groups[i] = ds.sel(time1=ds["ping_time"]).drop_vars("time1", errors="ignore")
return [set_time_encodings(ds) for ds in beam_groups]
def set_vendor(self) -> xr.Dataset:
ds = self._make_dataset(
{
"version": "data_record_version",
"pressure_sensor_valid": "pressure_sensor_valid",
"temperature_sensor_valid": "temperature_sensor_valid",
"compass_sensor_valid": "compass_sensor_valid",
"tilt_sensor_valid": "tilt_sensor_valid",
"velocity_data_included": "velocity_data_included",
"amplitude_data_included": "amplitude_data_included",
"correlation_data_included": "correlation_data_included",
"altimeter_data_included": "altimeter_data_included",
"altimeter_raw_data_included": "altimeter_raw_data_included",
"ast_data_included": "ast_data_included",
"echosounder_data_included": "echosounder_data_included",
"ahrs_data_included": "ahrs_data_included",
"percentage_good_data_included": "percentage_good_data_included",
"std_dev_data_included": "std_dev_data_included",
"distance_data_included": "distance_data_included",
"figure_of_merit_data_included": "figure_of_merit_data_included",
"error": "error",
"status0": "status0",
"procidle3": "procidle3",
"procidle6": "procidle6",
"procidle12": "procidle12",
"status": "status",
"wakeup_state": "wakeup_state",
"orientation": "orientation",
"autoorientation": "autoorientation",
"previous_wakeup_state": "previous_wakeup_state",
"last_measurement_low_voltage_skip": "last_measurement_low_voltage_skip",
"active_configuration": "active_configuration",
"echosounder_index": "echosounder_index",
"telemetry_data": "telemetry_data",
"boost_running": "boost_running",
"echosounder_frequency_bin": "echosounder_frequency_bin",
"bd_scaling": "bd_scaling",
"battery_voltage": "battery_voltage",
"power_level": "power_level",
"temperature_from_pressure_sensor": "temperature_of_pressure_sensor",
"nominal_correlation": "nominal_correlation",
"magnetometer_temperature": "magnetometer_temperature",
"real_time_clock_temperature": "real_time_clock_temperature",
"ensemble_counter": "ensemble_counter",
"ahrs_rotation_matrix": "ahrs_rotation_matrix_mij",
"ahrs_quaternions": "ahrs_quaternions_wxyz",
"ahrs_gyro": "ahrs_gyro_xyz",
"percentage_good_data": "percentage_good_data",
"std_dev_pitch": "std_dev_pitch",
"std_dev_roll": "std_dev_roll",
"std_dev_heading": "std_dev_heading",
"std_dev_pressure": "std_dev_pressure",
"pressure_sensor_valid": "pressure_sensor_valid",
"temperature_sensor_valid": "temperature_sensor_valid",
"compass_sensor_valid": "compass_sensor_valid",
"tilt_sensor_valid": "tilt_sensor_valid",
"figure_of_merit_data": "figure_of_merit",
"altimeter_distance": "altimeter_distance",
"altimeter_quality": "altimeter_quality",
"ast_distance": "ast_distance",
"ast_quality": "ast_quality",
"ast_offset_100us": "ast_offset_100us",
"ast_pressure": "ast_pressure",
"altimeter_spare": "altimeter_spare",
"altimeter_raw_data_num_samples": "altimeter_raw_data_num_samples",
"altimeter_raw_data_sample_distance": "altimeter_raw_data_sample_distance",
"altimeter_raw_data_samples": "altimeter_raw_data_samples",
"magnetometer_raw": "magnetometer_raw",
}
)
return set_time_encodings(ds)
def set_sonar(self) -> xr.Dataset:
"""Set the Sonar group."""
# Add beam_group and beam_group_descr variables sharing a common dimension
# (beam_group), using the information from self._beamgroups
beam_groups_vars, beam_groups_coord = self._beam_groups_vars()
ds = xr.Dataset(beam_groups_vars, coords=beam_groups_coord)
# Assemble sonar group global attribute dictionary
sonar_attr_dict = {
"sonar_manufacturer": "Nortek",
"sonar_model": "AD2CP",
"sonar_serial_number": ", ".join(
np.unique(
[
str(packet.data["serial_number"])
for packet in self.parser_obj.packets
if "serial_number" in packet.data
]
)
),
"sonar_software_name": "",
"sonar_software_version": "",
"sonar_firmware_version": "",
"sonar_type": "acoustic Doppler current profiler (ADCP)",
}
firmware_version = self.parser_obj.get_firmware_version()
if firmware_version is not None:
sonar_attr_dict["sonar_firmware_version"] = ", ".join(
[f"{k}:{v}" for k, v in firmware_version.items()]
)
ds = ds.assign_attrs(sonar_attr_dict)
return set_time_encodings(ds)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,768 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/utils/test_source_filenames.py | from pathlib import Path
import numpy as np
from echopype.utils.prov import _sanitize_source_files
def test_scalars():
"""One or more scalar values"""
path1 = "/my/path1"
path2 = Path("/my/path2")
# Single scalars
assert _sanitize_source_files(path1) == [path1]
assert _sanitize_source_files(path2) == [str(path2)]
# List of scalars
assert _sanitize_source_files([path1, path2]) == [path1, str(path2)]
def test_mixed():
"""A scalar value and a list or ndarray"""
path1 = "/my/path1"
path2 = Path("/my/path2")
# Mixed-type list
path_list1 = [path1, path2]
# String-type ndarray
path_list2 = np.array([path1, str(path2)])
# A scalar and a list
target_path_list = [path1, path1, str(path2)]
assert _sanitize_source_files([path1, path_list1]) == target_path_list
# A scalar and an ndarray
assert _sanitize_source_files([path1, path_list2]) == target_path_list
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,769 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/convention/__init__.py | from .conv import _Convention
# Instantiate the singleton
sonarnetcdf_1 = _Convention(version="1.0")
__all__ = ["sonarnetcdf_1"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,770 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/consolidate/split_beam_angle.py | """
Contains functions necessary to compute the split-beam (alongship/athwartship)
angles and add them to a Dataset.
"""
from typing import List, Optional, Tuple
import numpy as np
import xarray as xr
from ..calibrate.ek80_complex import compress_pulse, get_norm_fac, get_transmit_signal
def _compute_angle_from_complex(
bs: xr.DataArray, beam_type: int, sens: List[xr.DataArray], offset: List[xr.DataArray]
) -> Tuple[xr.DataArray, xr.DataArray]:
"""
Compute split-beam angles from raw data from transducer sectors.
Can be used for data from a single channel or multiple channels,
depending on what is in ``bs``.
Parameters
----------
bs: xr.DataArray
Complex backscatter samples from a single channel or multiple channels
beam_type: int
The type of beam being considered
sens: list of xr.DataArray
A list of length two where the first element corresponds to the
angle sensitivity alongship and the second corresponds to the
angle sensitivity athwartship
offset: list of xr.DataArray
A list of length two where the first element corresponds to the
angle offset alongship and the second corresponds to the
angle offset athwartship
Returns
-------
theta: xr.DataArray
The calculated split-beam alongship angle for a specific channel
phi: xr.DataArray
The calculated split-beam athwartship angle for a specific channel
Notes
-----
This function should only be used for data with complex backscatter.
"""
# 4-sector transducer
if beam_type == 1:
bs_fore = (bs.isel(beam=2) + bs.isel(beam=3)) / 2 # forward
bs_aft = (bs.isel(beam=0) + bs.isel(beam=1)) / 2 # aft
bs_star = (bs.isel(beam=0) + bs.isel(beam=3)) / 2 # starboard
bs_port = (bs.isel(beam=1) + bs.isel(beam=2)) / 2 # port
bs_theta = bs_fore * np.conj(bs_aft)
bs_phi = bs_star * np.conj(bs_port)
theta = np.arctan2(np.imag(bs_theta), np.real(bs_theta)) / np.pi * 180
phi = np.arctan2(np.imag(bs_phi), np.real(bs_phi)) / np.pi * 180
# 3-sector transducer with or without center element
elif beam_type in [17, 49, 65, 81]:
# 3-sector
if beam_type == 17:
bs_star = bs.isel(beam=0)
bs_port = bs.isel(beam=1)
bs_fore = bs.isel(beam=2)
else:
# 3-sector + 1 center element
bs_star = (bs.isel(beam=0) + bs.isel(beam=3)) / 2
bs_port = (bs.isel(beam=1) + bs.isel(beam=3)) / 2
bs_fore = (bs.isel(beam=2) + bs.isel(beam=3)) / 2
bs_fac1 = bs_fore * np.conj(bs_star)
bs_fac2 = bs_fore * np.conj(bs_port)
fac1 = np.arctan2(np.imag(bs_fac1), np.real(bs_fac1)) / np.pi * 180
fac2 = np.arctan2(np.imag(bs_fac2), np.real(bs_fac2)) / np.pi * 180
theta = (fac1 + fac2) / np.sqrt(3)
phi = fac2 - fac1
# EC150–3C
elif beam_type == 97:
raise NotImplementedError
else:
raise ValueError("beam_type not recognized!")
theta = theta / sens[0] - offset[0]
phi = phi / sens[1] - offset[1]
return theta, phi
def get_angle_power_samples(
ds_beam: xr.Dataset, angle_params: dict
) -> Tuple[xr.Dataset, xr.Dataset]:
"""
Obtain split-beam angle from CW mode power samples.
Parameters
----------
ds_beam: xr.Dataset
An ``EchoData`` Sonar/Beam_group1 group (complex samples always in Beam_group1)
angle_params : dict
A dictionary containing angle_offset/angle_sensitivity parameters
from the calibrated dataset
Returns
-------
theta: xr.Dataset
Split-beam alongship angle
phi: xr.Dataset
Split-beam athwartship angle
Notes
-----
Can be used on both EK60 and EK80 data
Computation done for ``beam_type=1``:
``physical_angle = ((raw_angle * 180 / 128) / sensitivity) - offset``
"""
# raw_angle scaling constant
conversion_const = 180.0 / 128.0
def _e2f(angle_type: str) -> xr.Dataset:
"""Convert electric angle to physical angle for split-beam data"""
return (
conversion_const
* ds_beam[f"angle_{angle_type}"]
/ angle_params[f"angle_sensitivity_{angle_type}"]
- angle_params[f"angle_offset_{angle_type}"]
)
# add split-beam angle if at least one channel is split-beam
# in the case when some channels are split-beam and some single-beam
# the single-beam channels will be all NaNs and _e2f would run through and output NaNs
if not np.all(ds_beam["beam_type"].data == 0):
theta = _e2f(angle_type="alongship") # split-beam alongship angle
phi = _e2f(angle_type="athwartship") # split-beam athwartship angle
else:
raise ValueError(
"Computing physical split-beam angle is only available for data "
"from split-beam transducers!"
)
return theta, phi
def get_angle_complex_samples(
ds_beam: xr.Dataset, angle_params: dict, pc_params: dict = None
) -> Tuple[xr.DataArray, xr.DataArray]:
"""
Obtain split-beam angle from CW or BB mode complex samples.
Parameters
----------
ds_beam : xr.Dataset
An ``EchoData`` Sonar/Beam_group1 group (complex samples always in Beam_group1)
angle_params : dict
A dictionary containing angle_offset/angle_sensitivity parameters
from the calibrated dataset
pc_params : dict
Parameters needed for pulse compression
This dict also serves as a flag for whether to apply pulse compression
Returns
-------
theta : xr.Dataset
Split-beam alongship angle
phi : xr.Dataset
Split-beam athwartship angle
"""
# Get complex backscatter samples
bs = ds_beam["backscatter_r"] + 1j * ds_beam["backscatter_i"]
# Pulse compression if pc_params exists
if pc_params is not None:
tx, tx_time = get_transmit_signal(
beam=ds_beam,
coeff=pc_params, # this is filter_coeff with fs added
waveform_mode="BB",
fs=pc_params["receiver_sampling_frequency"], # this is the added fs
)
bs = compress_pulse(backscatter=bs, chirp=tx) # has beam dim
bs = bs / get_norm_fac(chirp=tx) # normalization for each channel
# Compute angles
# unique beam_type existing in the dataset
beam_type_all_ch = np.unique(ds_beam["beam_type"].data)
if beam_type_all_ch.size == 1:
# If beam_type is the same for all channels, process all channels at once
theta, phi = _compute_angle_from_complex(
bs=bs,
beam_type=beam_type_all_ch[0], # beam_type for all channels
sens=[
angle_params["angle_sensitivity_alongship"],
angle_params["angle_sensitivity_athwartship"],
],
offset=[
angle_params["angle_offset_alongship"],
angle_params["angle_offset_athwartship"],
],
)
else:
# beam_type different for some channels, process each channel separately
theta, phi = [], []
for ch_id in bs["channel"].data:
theta_ch, phi_ch = _compute_angle_from_complex(
bs=bs.sel(channel=ch_id),
# beam_type is not time-varying
beam_type=(ds_beam["beam_type"].sel(channel=ch_id)),
sens=[
angle_params["angle_sensitivity_alongship"].sel(channel=ch_id),
angle_params["angle_sensitivity_athwartship"].sel(channel=ch_id),
],
offset=[
angle_params["angle_offset_alongship"].sel(channel=ch_id),
angle_params["angle_offset_athwartship"].sel(channel=ch_id),
],
)
theta.append(theta_ch)
phi.append(phi_ch)
# Combine angles from all channels
theta = xr.DataArray(
data=theta,
coords={
"channel": bs["channel"],
"ping_time": bs["ping_time"],
"range_sample": bs["range_sample"],
},
)
phi = xr.DataArray(
data=phi,
coords={
"channel": bs["channel"],
"ping_time": bs["ping_time"],
"range_sample": bs["range_sample"],
},
)
return theta, phi
def add_angle_to_ds(
theta: xr.Dataset,
phi: xr.Dataset,
ds: xr.Dataset,
return_dataset: bool,
source_ds_path: Optional[str] = None,
file_type: Optional[str] = None,
storage_options: dict = {},
) -> Optional[xr.Dataset]:
"""
Adds the split-beam angle data to the provided input ``ds``.
Parameters
----------
theta: xr.Dataset
The calculated split-beam alongship angle
phi: xr.Dataset
The calculated split-beam athwartship angle
ds: xr.Dataset
The Dataset that ``theta`` and ``phi`` will be added to
return_dataset: bool
Whether a dataset will be returned or not
source_ds_path: str, optional
The path to the file corresponding to ``ds``, if it exists
file_type: {"netcdf4", "zarr"}, optional
The file type corresponding to ``source_ds_path``
storage_options: dict, default={}
Any additional parameters for the storage backend, corresponding to the
path ``source_ds_path``
Returns
-------
xr.Dataset or None
If ``return_dataset=False``, nothing will be returned. If ``return_dataset=True``
either the input dataset ``ds`` or a lazy-loaded Dataset (obtained from
the path provided by ``source_ds_path``) with the split-beam angle data added
will be returned.
"""
# TODO: do we want to add anymore attributes to these variables?
# add appropriate attributes to theta and phi
theta.attrs["long_name"] = "split-beam alongship angle"
phi.attrs["long_name"] = "split-beam athwartship angle"
if source_ds_path is not None:
# put the variables into a Dataset, so they can be written at the same time
# add ds attributes to splitb_ds since they will be overwritten by to_netcdf/zarr
splitb_ds = xr.Dataset(
data_vars={"angle_alongship": theta, "angle_athwartship": phi},
coords=theta.coords,
attrs=ds.attrs,
)
# release any resources linked to ds (necessary for to_netcdf)
ds.close()
# write the split-beam angle data to the provided path
if file_type == "netcdf4":
splitb_ds.to_netcdf(path=source_ds_path, mode="a", **storage_options)
else:
splitb_ds.to_zarr(store=source_ds_path, mode="a", **storage_options)
if return_dataset:
# open up and return Dataset in source_ds_path
return xr.open_dataset(source_ds_path, engine=file_type, chunks={}, **storage_options)
else:
# add the split-beam angles to the provided Dataset
ds["angle_alongship"] = theta
ds["angle_athwartship"] = phi
if return_dataset:
# return input dataset with split-beam angle data
return ds
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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,771 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/ecs.py | import re
from collections import defaultdict
from datetime import datetime
from typing import Dict, Literal, Optional, Tuple, Union
import numpy as np
import xarray as xr
from ..utils.log import _init_logger
logger = _init_logger(__name__)
# String matcher for parser
SEPARATOR = re.compile(r"#=+#\n")
STATUS_CRUDE = re.compile(r"#\s*(?P<status>(.+))\s*#\n") # noqa
STATUS_FINE = re.compile(r"#\s+(?P<status>\w+) SETTINGS\s*#\n") # noqa
ECS_HEADER = re.compile(
r"#\s*ECHOVIEW CALIBRATION SUPPLEMENT \(.ECS\) FILE \((?P<data_type>.+)\)\s*#\n" # noqa
)
ECS_TIME = re.compile(
r"#\s+(?P<date>\d{1,2}\/\d{1,2}\/\d{4}) (?P<time>\d{1,2}\:\d{1,2}\:\d{1,2})(.\d+)?\s+#\n" # noqa
)
ECS_VERSION = re.compile(r"Version (?P<version>\d+\.\d+)\s*\n") # noqa
PARAM_MATCHER = re.compile(
# r"\s*(?P<skip>#?)\s*(?P<param>\w+)\s*=\s*(?P<val>((-?\d+(?:\.\d+))|\w+)?)?\s*#?(.*)\n" # noqa
# can be multiple values separated by space
r"\s*(?P<skip>#?)\s*(?P<param>\w+)\s*=\s*(?P<val>((-?\d+(?:\.\d+)\s*)+|\w+)?)?\s*#?(.*)\n" # noqa
)
VAL_PATTERN = r"(-?\d+(?:\.\d+)\s*)\s+"
CAL_HIERARCHY = re.compile(
r"(SourceCal|LocalCal) (?P<source>\w+)\s*\n", re.I
) # ignore case # noqa
# Convert dict from ECS to echopype format
EV_EP_MAP = {
# from Echoview-generated ECS template (unless noted otherwise) : EchoData variable name
# Ex60 / Ex70 / EK15
"EK60": {
"AbsorptionCoefficient": "sound_absorption",
"Frequency": "frequency_nominal", # will use for checking channel and freq match
"MajorAxis3dbBeamAngle": "beamwidth_athwartship",
"MajorAxisAngleOffset": "angle_offset_athwartship",
"MajorAxisAngleSensitivity": "angle_sensitivity_athwartship",
"MinorAxis3dbBeamAngle": "beamwidth_alongship",
"MinorAxisAngleOffset": "angle_offset_alongship",
"MinorAxisAngleSensitivity": "angle_sensitivity_alongship",
"PulseDuration": "transmit_duration_nominal",
"SaCorrectionFactor": "sa_correction",
"SoundSpeed": "sound_speed",
"EK60SaCorrection": "sa_correction", # from NWFSC template
"TransducerGain": "gain_correction",
"Ek60TransducerGain": "gain_correction", # from NWFSC template
"TransmittedPower": "transmit_power",
# "TvgRangeCorrection": "tvg_range_correction", # not in EchoData
# "TvgRangeCorrectionOffset": "tvg_range_correction_offset", # not in EchoData
"TwoWayBeamAngle": "equivalent_beam_angle",
},
# Additional on EK80, ES80, WBAT, EA640
# Note these should be concat after the EK60 dict
"EK80": {
"AbsorptionDepth": "pressure",
"Acidity": "pH",
"EffectivePulseDuration": "tau_effective",
"Salinity": "salinity",
"SamplingFrequency": "sampling_frequency", # does not exist in echopype.EchoData
"Temperature": "temperature",
"TransceiverImpedance": "impedance_transceiver",
"TransceiverSamplingFrequency": "receiver_sampling_frequency",
# "TransducerModeActive": "transducer_mode", # TODO: CHECK NAME IN ECHODATA
"FrequencyTableWideband": "frequency_BB", # frequency axis for broadband cal params
"GainTableWideband": "gain_correction", # freq-dep
"MajorAxisAngleOffsetTableWideband": "angle_offset_athwartship", # freq-dep
"MajorAxisBeamWidthTableWideband": "beamwidth_athwartship", # freq-dep
"MinorAxisAngleOffsetTableWideband": "angle_offset_alongship", # freq-dep
"MinorAxisBeamWidthTableWideband": "beamwidth_alongship", # freq-dep
"NumberOfTransducerSegments": "n_sector", # TODO: CHECK IN ECHODATA
"PulseCompressedEffectivePulseDuration": "tau_effective_pc", # TODO: not in EchoData
},
# AZFP-specific
# Note: not sure why it doesn't contain salinity and pressure required for computing absorption
# "AZFP": {
# AzfpDetectionSlope = 0.023400 # [0.000000..1.000000]
# AzfpEchoLevelMax = 142.8 # (decibels) [0.0..9999.0]
# AzfpTransmitVoltage = 53.0 # [0.0..999.0]
# AzfpTransmitVoltageResponse = 170.9 # (decibels) [0.0..999.0]
# Frequency = 38.00 # (kilohertz) [0.01..10000.00]
# PulseDuration = 1.000 # (milliseconds) [0.001..200.000]
# SoundSpeed = 1450.50 # (meters per second) [1400.00..1700.00]
# TwoWayBeamAngle = -16.550186 # (decibels re 1 steradian) [-99.000000..11.000000]
# TvgRangeCorrection = # [None, BySamples, SimradEx500, SimradEx60, BioSonics, Kaijo, PulseLength, Ex500Forced, SimradEK80, Standard] # noqa
# TvgRangeCorrectionOffset = # (samples) [-10000.00..10000.00]
# },
}
ENV_PARAMS = [
"AbsorptionCoefficient",
"SoundSpeed",
"AbsorptionDepth",
"Acidity",
"Salinity",
"Temperature",
]
# Used in ecs_ev2ep to assemble xr.DataArray for freq-dep BB params
CAL_PARAMS_BB = (
"FrequencyTableWideband",
"GainTableWideband",
"MajorAxisAngleOffsetTableWideband",
"MajorAxisBeamWidthTableWideband",
"MinorAxisAngleOffsetTableWideband",
"MinorAxisBeamWidthTableWideband",
)
class ECSParser:
"""
Class for parsing Echoview calibration supplement (ECS) files.
"""
TvgRangeCorrection_allowed_str = (
"None",
"BySamples",
"SimradEx500",
"SimradEx60",
"BioSonics",
"Kaijo",
"PulseLength",
"Ex500Forced",
"SimradEK80",
"Standard",
)
def __init__(self, input_file=None):
self.input_file = input_file
self.data_type = None
self.version = None
self.file_creation_time: Optional[datetime] = None
self.parsed_params: Optional[dict] = None
def _parse_header(self, fid) -> bool:
"""
Parse header block.
"""
tmp = ECS_TIME.match(fid.readline())
self.file_creation_time = datetime.strptime(
tmp["date"] + " " + tmp["time"], "%m/%d/%Y %H:%M:%S"
)
if SEPARATOR.match(fid.readline()) is None: # line 4: separator
raise ValueError("Unexpected line in ECS file!")
# line 5-10: skip
[fid.readline() for ff in range(6)]
if SEPARATOR.match(fid.readline()) is None: # line 11: separator
raise ValueError("Unexpected line in ECS file!")
# read lines until seeing version number
line = "\n"
while line == "\n":
line = fid.readline()
self.version = ECS_VERSION.match(line)["version"]
return True
def _parse_block(self, fid, status) -> dict:
"""
Parse the FileSet, SourceCal or LocalCal block.
Parameters
----------
fid : File Object
status : str {"sourcecal", "localcal"}
"""
param_val = dict()
if SEPARATOR.match(fid.readline()) is None: # skip 1 separator line
raise ValueError("Unexpected line in ECS file!")
source = None
cont = True
while cont:
curr_pos = fid.tell() # current position
line = fid.readline()
if SEPARATOR.match(line) is not None:
# reverse to previous position and jump out
fid.seek(curr_pos)
cont = False
elif line == "": # EOF
break
else:
if status == "fileset" and source is None:
source = "fileset" # force this for easy organization
param_val[source] = dict()
elif status in line.lower(): # {"sourcecal", "localcal"}
source = CAL_HIERARCHY.match(line)["source"]
param_val[source] = dict()
else:
if line != "\n" and source is not None:
tmp = PARAM_MATCHER.match(line)
if tmp["skip"] == "" or tmp["param"] == "Frequency": # not skipping
param_val[source][tmp["param"]] = tmp["val"]
return param_val
def _convert_param_type(self):
"""
Convert data type for all parameters.
"""
def convert_type(input_dict):
for k, v in input_dict.items():
if k == "TvgRangeCorrection":
if v not in self.TvgRangeCorrection_allowed_str:
raise ValueError("TvgRangeCorrection contains unexpected setting!")
elif k == "TransducerModeActive":
input_dict[k] = bool(v)
else:
val_rep = re.findall(VAL_PATTERN, v) # only match numbers
if len(val_rep) > 1: # many values (ie a vector)
input_dict[k] = np.array(val_rep).astype(float)
else:
input_dict[k] = float(v)
for status, status_settings in self.parsed_params.items():
if status == "fileset": # fileset only has 1 layer of dict
convert_type(status_settings)
else: # sourcecal or localcal has another layer of dict
for src_k, src_v in status_settings.items():
convert_type(src_v)
def parse(self):
"""
Parse the entire ECS file.
"""
fid = open(self.input_file, encoding="utf-8-sig")
line = fid.readline()
parsed_params = dict()
status = None # status = {"ecs", "fileset", "sourcecal", "localcal"}
while line != "": # EOF: line=""
if line != "\n": # skip empty line
if SEPARATOR.match(line) is not None:
if status is not None: # entering another block
status = None
elif status is None: # going into a block
status_str = STATUS_CRUDE.match(line)["status"].lower()
if "ecs" in status_str:
status = "ecs"
self.data_type = ECS_HEADER.match(line)["data_type"] # get data type
self._parse_header(fid)
elif (
"fileset" in status_str
or "sourcecal" in status_str
or "localcal" in status_str
):
status = STATUS_FINE.match(line)["status"].lower()
parsed_params[status] = self._parse_block(fid, status)
else:
raise ValueError("Expecting a new block but got something else!")
line = fid.readline() # read next line
# Make FileSet settings dict less awkward
parsed_params["fileset"] = parsed_params["fileset"]["fileset"]
# Store params
self.parsed_params = parsed_params
# Convert parameter type to float
self._convert_param_type()
def get_cal_params(self, localcal_name=None) -> dict():
"""
Get a consolidated set of calibration parameters that is applied to data by Echoview.
The calibration settings in Echoview have an overwriting hierarchy as documented
`here <https://support.echoview.com/WebHelp/Reference/File_formats/Echoview_calibration_supplement_files.html>`_. # noqa
Parameters
----------
localcal_name : str or None
Name of the LocalCal settings selected in Echoview.
Default is the first one read in the ECS file.
Returns
-------
A dictionary containing calibration parameters as interpreted by Echoview.
"""
# Create template based on sources
sources = self.parsed_params["sourcecal"].keys()
ev_cal_params = dict().fromkeys(sources)
# FileSet settings: apply to all sources
for src in sources:
ev_cal_params[src] = self.parsed_params["fileset"].copy()
# SourceCal settings: overwrite FileSet settings for each source
for src in sources:
for k, v in self.parsed_params["sourcecal"][src].items():
ev_cal_params[src][k] = v
# LocalCal settings: overwrite the above settings for all sources
if self.parsed_params["localcal"] != {}:
if localcal_name is None: # use the first LocalCal setting by default
localcal_name = list(self.parsed_params["localcal"].keys())[0]
for k, v in self.parsed_params["localcal"][localcal_name].items():
for src in sources:
ev_cal_params[src][k] = v
return ev_cal_params
def ecs_ev2ep(
ev_dict: Dict[str, Union[int, float, str]],
sonar_type: Literal["EK60", "EK80", "AZFP"],
) -> Tuple[xr.Dataset, xr.Dataset, Union[None, xr.Dataset]]:
"""
Convert dictionary from consolidated ECS form to xr.DataArray expected by echopype.
Parameters
----------
ev_dict : dict
A dictionary of the format parsed by the ECS parser
sonar_type : str
Type of sonar, must be one of {}"EK60", "EK80", "AZFP"}
Returns
-------
env_dict : xr.Dataset
An xr.Dataset containing environmental parameters
cal_dict : xr.Dataset
An xr.Dataset containing calibration parameters
cal_dict_BB : xr.Dataset or None
An xr.Dataset containing frequency-dependent calibration parameters (EK80 only)
"""
# Set up allowable cal or env variables
if sonar_type[:2] == "EK":
PARAM_MAP = EV_EP_MAP["EK60"]
if sonar_type == "EK80":
PARAM_MAP = dict(PARAM_MAP, **EV_EP_MAP["EK80"])
# all params - env params = cal params
CAL_PARAMS = set(PARAM_MAP.keys()).difference(set(ENV_PARAMS))
# remove freq-dep ones
CAL_PARAMS = set(CAL_PARAMS).difference(CAL_PARAMS_BB)
def get_param_ds(param_type):
dict_out = defaultdict(list)
for p_name in param_type:
param_tmp = []
for source, source_dict in ev_dict.items(): # all transducers
if p_name in source_dict:
param_tmp.append(source_dict[p_name])
else:
param_tmp.append(np.nan)
if not np.isnan(param_tmp).all(): # only keep param if not all NaN
dict_out[PARAM_MAP[p_name]] = (["channel"], param_tmp)
return xr.Dataset(data_vars=dict_out, coords={"channel": np.arange(len(ev_dict))})
# Scalar params: add dimension to dict and assemble DataArray
ds_env = get_param_ds(ENV_PARAMS)
ds_cal = get_param_ds(CAL_PARAMS)
ds_env["frequency_nominal"] = ds_cal["frequency_nominal"] # used for checking later
# Vector params (frequency-dep params)
ds_cal_BB = []
for source, source_dict in ev_dict.items():
if "FrequencyTableWideband" in source_dict:
param_dict = {}
for p_name in CAL_PARAMS_BB:
if p_name in source_dict: # only for param that exists in dict
param_dict[PARAM_MAP[p_name]] = (["cal_frequency"], source_dict[p_name])
ds_ch = xr.Dataset(
data_vars=param_dict,
coords={
"cal_frequency": (
["cal_frequency"],
source_dict["FrequencyTableWideband"],
{
"long_name": "Frequency of calibration parameter",
"units": "Hz",
},
)
},
)
ds_ch = ds_ch.drop_vars("frequency_BB")
ds_ch = ds_ch.expand_dims({"frequency_nominal": [source_dict["Frequency"]]})
ds_cal_BB.append(ds_ch)
ds_cal_BB = xr.merge(ds_cal_BB) if len(ds_cal_BB) != 0 else None
# Convert frequency variables from kHz to Hz
for p_name in ["frequency_nominal", "sampling_frequency", "receiver_sampling_frequency"]:
for ds in [ds_env, ds_cal, ds_cal_BB]:
if ds is not None and p_name in ds:
ds[p_name] = ds[p_name] * 1000
return ds_env, ds_cal, ds_cal_BB
def ecs_ds2dict(ds: xr.Dataset) -> Dict:
"""
Convert an xr.Dataset to a dictionary with each data variable being a key-value pair.
"""
dict_tmp = {}
for data_var_name in ds.data_vars:
dict_tmp[data_var_name] = ds[data_var_name]
return dict_tmp
def conform_channel_order(ds_in: xr.Dataset, freq_ref: xr.DataArray) -> xr.Dataset:
"""
Check the sequence of channels against a set of reference channels and reorder if necessary.
Parameters
----------
ds_in : xr.Dataset
An xr.Dataset generated by ``ev2ep_dict``.
It must contain 'frequency_nominal' as a data variable and 'channel' as a dimension.
freq_ref : xr.DataArray
An xr.DataArray containing the nominal frequency in the order to be conformed with.
It must contain 'channel' as a coordinate.
Returns
-------
xr.Dataset or None
An xr.Dataset containing channels (frequencies) that are the intersection of ``freq_ref``
and ``ds_in``, with the channel aligned in the same order as the subset of ``freq_ref``.
None is returned if there is no overlapping frequency between ``freq_ref`` and ``ds_in``.
Notes
-----
If ``ds_in`` contains more channels than ``freq_ref``, only those present in ``freq_ref``
are selected and retained.
Raises
------
ValueError
If ``freq_ref`` is not an xr.DataArray
ValueError
If ``freq_ref`` does not contain ``channel` as a coordinate
"""
# freq_ref must be an xr.DataArray with channel as a coordinate
if not isinstance(freq_ref, xr.DataArray):
raise ValueError("'freq_ref' has to be an xr.DataArray!")
else:
if "channel" not in freq_ref.coords:
raise ValueError("'channel' has to be a coordinate 'freq_ref'!")
# ds_in must be an xr.Dataset with either channel or frequency_nominal as a coordinate
if "channel" not in ds_in.coords and "frequency_nominal" not in ds_in.coords:
raise ValueError(
"'ds_in' must be an xr.Dataset with either 'channel' or 'frequency_nominal' "
"as a coordinate!"
)
# Get frequencies that exist in both freq_req and ds_in
freq_overlap = list(set(freq_ref.values) & set(ds_in["frequency_nominal"].values))
if len(freq_overlap) == 0: # no overlapping frequency
return None
else:
# need to sort freq_overlap according to freq_ref order
freq_overlap = [f for f in freq_ref.values if f in freq_overlap]
# Set both freq_ref and ds_in to align with frequency
freq_ref.name = "frequency_nominal"
freq_ref = (
freq_ref.to_dataset()
.set_coords("frequency_nominal")
.swap_dims({"channel": "frequency_nominal"})
.sel(frequency_nominal=freq_overlap) # subset
)
# channel is coordinate, swap to align with frequency_nominal
if "frequency_nominal" not in ds_in.coords:
ds_in = ds_in.set_coords("frequency_nominal").swap_dims(
{"channel": "frequency_nominal"}
)
ds_in = ds_in.sel(frequency_nominal=freq_overlap) # subset
# Reorder according to the frequency dimension, this will subset ds_in as well
ds_in = ds_in.reindex_like(freq_ref)
ds_in["channel"] = freq_ref["channel"]
return ds_in.swap_dims({"frequency_nominal": "channel"}).drop("frequency_nominal")
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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"/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,772 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_ecs_integration.py | import pytest
import numpy as np
import xarray as xr
import echopype as ep
from echopype.calibrate.ecs import ECSParser, ecs_ev2ep, ecs_ds2dict, conform_channel_order
from echopype.calibrate.env_params import get_env_params_EK
from echopype.calibrate.cal_params import get_cal_params_EK
# @pytest.fixture
# def azfp_path(test_path):
# return test_path['AZFP']
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
@pytest.fixture
def ecs_path(test_path):
return test_path['ECS']
@pytest.fixture
def ek80_path(test_path):
return test_path['EK80']
# @pytest.fixture
# def ek80_cal_path(test_path):
# return test_path['EK80_CAL']
# @pytest.fixture
# def ek80_ext_path(test_path):
# return test_path['EK80_EXT']
def test_ecs_intake_ek60(ek60_path, ecs_path):
# get EchoData object that has the water_level variable under platform and compute Sv of it
ed = ep.open_raw(ek60_path / "ncei-wcsd" / "Summer2017-D20170620-T011027.raw", "EK60")
ecs_file = ecs_path / "Summer2017_JuneCal_3freq_mod.ecs"
ds_Sv = ep.calibrate.compute_Sv(ed, ecs_file=ecs_file)
# Parse ECS separately
ecs = ECSParser(ecs_file)
ecs.parse()
ecs_dict = ecs.get_cal_params() # apply ECS hierarchy
ds_env_tmp, ds_cal_tmp, _ = ecs_ev2ep(ecs_dict, "EK60")
env_params = ecs_ds2dict(conform_channel_order(ds_env_tmp, ed["Sonar/Beam_group1"]["frequency_nominal"]))
cal_params = ecs_ds2dict(conform_channel_order(ds_cal_tmp, ed["Sonar/Beam_group1"]["frequency_nominal"]))
# Check if the final stored params (which are those used in calibration operations)
# are those parsed from ECS
for p_name in ["sound_speed", "sound_absorption"]:
assert "ping_time" not in ds_Sv[p_name] # only if pull from data will params have time coord
assert ds_Sv[p_name].identical(env_params[p_name])
for p_name in [
"sa_correction", "gain_correction", "equivalent_beam_angle",
"beamwidth_alongship", "beamwidth_athwartship",
"angle_offset_alongship", "angle_offset_athwartship",
"angle_sensitivity_alongship", "angle_sensitivity_athwartship"
]:
assert ds_Sv[p_name].identical(cal_params[p_name])
# EK80 CW power
def test_ecs_intake_ek80_CW_power(ek80_path, ecs_path):
# get EchoData object that has the water_level variable under platform and compute Sv of it
ed = ep.open_raw(ek80_path / "Summer2018--D20180905-T033113.raw", sonar_model="EK80")
ecs_file = ecs_path / "Simrad_EK80_ES80_WBAT_EKAuto_Kongsberg_EA640_nohash.ecs"
ds_Sv = ep.calibrate.compute_Sv(ed, ecs_file=ecs_file, waveform_mode="CW", encode_mode="power")
# Parse ECS separately
ecs = ECSParser(ecs_file)
ecs.parse()
ecs_dict = ecs.get_cal_params() # apply ECS hierarchy
ds_env, ds_cal_NB, ds_cal_BB = ecs_ev2ep(ecs_dict, "EK80")
beam = ed["Sonar/Beam_group2"]
chan_sel = ["WBT 743366-15 ES38B_ES", "WBT 743367-15 ES18_ES"]
ecs_env_params = ecs_ds2dict(
conform_channel_order(ds_env, beam["frequency_nominal"].sel(channel=chan_sel))
)
ecs_cal_params = ecs_ds2dict(
conform_channel_order(ds_cal_NB, beam["frequency_nominal"].sel(channel=chan_sel))
)
ecs_cal_tmp_BB_conform_freq = conform_channel_order(
ds_cal_BB, beam["frequency_nominal"].sel(channel=chan_sel)
)
assert ecs_cal_tmp_BB_conform_freq is None
# Assimilate to standard env_params and cal_params in cal object
assimilated_env_params = get_env_params_EK(
sonar_type="EK80",
beam=beam,
env=ed["Environment"],
user_dict=ecs_env_params,
freq=beam["frequency_nominal"].sel(channel=chan_sel),
)
# Check the final stored params (which are those used in calibration operations)
# For those pulled from ECS
for p_name in ["sound_speed", "temperature", "salinity", "pressure", "pH"]:
assert ds_Sv[p_name].identical(ecs_env_params[p_name])
for p_name in [
"sa_correction", "gain_correction", "equivalent_beam_angle",
"beamwidth_alongship", "beamwidth_athwartship",
"angle_offset_alongship", "angle_offset_athwartship",
"angle_sensitivity_alongship", "angle_sensitivity_athwartship"
]:
assert ds_Sv[p_name].identical(ecs_cal_params[p_name])
# For those computed from values in ECS file
assert np.all(ds_Sv["sound_absorption"].values == assimilated_env_params["sound_absorption"].values)
# TODO: remove params that are only relevant to EK80 complex sample cals
# `impedance_transducer`, `impedance_transceiver`, `receiver_sampling_frequency`
# for p_name in ["impedance_transducer", "impedance_transceiver", "receiver_sampling_frequency"]:
# assert p_name not in ds_Sv
# EK80 BB complex
def test_ecs_intake_ek80_BB_complex(ek80_path, ecs_path):
# get EchoData object that has the water_level variable under platform and compute Sv of it
ed = ep.open_raw(ek80_path / "Summer2018--D20180905-T033113.raw", sonar_model="EK80")
ecs_file = ecs_path / "Simrad_EK80_ES80_WBAT_EKAuto_Kongsberg_EA640_nohash.ecs"
ds_Sv = ep.calibrate.compute_Sv(ed, ecs_file=ecs_file, waveform_mode="BB", encode_mode="complex")
# Parse ECS separately
ecs = ECSParser(ecs_file)
ecs.parse()
ecs_dict = ecs.get_cal_params() # apply ECS hierarchy
ecs_env, ecs_cal_NB, ecs_cal_BB = ecs_ev2ep(ecs_dict, "EK80")
# chan_sel = ['WBT 545612-15 ES200-7C_ES', 'WBT 549762-15 ES70-7C_ES', 'WBT 743869-15 ES120-7C_ES']
ecs_env = conform_channel_order(ecs_env, ds_Sv["frequency_nominal"])
ecs_cal_NB = conform_channel_order(ecs_cal_NB, ds_Sv["frequency_nominal"])
ecs_cal_BB = conform_channel_order(ecs_cal_BB, ds_Sv["frequency_nominal"])
# Check the final stored params (which are those used in calibration operations)
# For those pulled from ECS
for p_name in ["sound_speed", "temperature", "salinity", "pressure", "pH"]:
assert ds_Sv[p_name].identical(ecs_env[p_name])
for p_name in ["sa_correction", "receiver_sampling_frequency"]:
assert ds_Sv[p_name].identical(ecs_cal_NB[p_name])
# Check interpolation was done correctly
beam = ed["Sonar/Beam_group1"]
chan_w_BB_param = "WBT 549762-15 ES70-7C_ES"
freq_center = (
(beam["transmit_frequency_start"] + beam["transmit_frequency_stop"]) / 2
).sel(channel=chan_w_BB_param).drop_vars(["channel"])
for p_name in [
"gain_correction", "angle_offset_alongship", "angle_offset_athwartship",
"beamwidth_alongship", "beamwidth_athwartship",
]:
assert ds_Sv[p_name].sel(channel=chan_w_BB_param).drop_vars("channel").identical(
ecs_cal_BB[p_name].interp(cal_frequency=freq_center)
.squeeze().drop_vars(["channel", "cal_frequency"])
)
# TODO: remove params that are only relevant to EK80 CW cals `sa_correction`
# assert "sa_correction" not in ds_Sv | {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,773 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/api.py | import xarray as xr
from ..echodata import EchoData
from ..echodata.simrad import check_input_args_combination
from ..utils.log import _init_logger
from ..utils.prov import echopype_prov_attrs, source_files_vars
from .calibrate_azfp import CalibrateAZFP
from .calibrate_ek import CalibrateEK60, CalibrateEK80
CALIBRATOR = {
"EK60": CalibrateEK60,
"EK80": CalibrateEK80,
"AZFP": CalibrateAZFP,
"ES70": CalibrateEK60,
"ES80": CalibrateEK80,
"EA640": CalibrateEK80,
}
logger = _init_logger(__name__)
def _compute_cal(
cal_type,
echodata: EchoData,
env_params=None,
cal_params=None,
ecs_file=None,
waveform_mode=None,
encode_mode=None,
):
# Check on waveform_mode and encode_mode inputs
if echodata.sonar_model == "EK80":
if waveform_mode is None or encode_mode is None:
raise ValueError("waveform_mode and encode_mode must be specified for EK80 calibration")
check_input_args_combination(waveform_mode, encode_mode)
elif echodata.sonar_model in ("EK60", "AZFP"):
if waveform_mode is not None and waveform_mode != "CW":
logger.warning(
"This sonar model transmits only narrowband signals (waveform_mode='CW'). "
"Calibration will be in CW mode",
)
if encode_mode is not None and encode_mode != "power":
logger.warning(
"This sonar model only record data as power or power/angle samples "
"(encode_mode='power'). Calibration will be done on the power samples.",
)
# Set up calibration object
cal_obj = CALIBRATOR[echodata.sonar_model](
echodata,
env_params=env_params,
cal_params=cal_params,
ecs_file=ecs_file,
waveform_mode=waveform_mode,
encode_mode=encode_mode,
)
# Perform calibration
if cal_type == "Sv":
cal_ds = cal_obj.compute_Sv()
elif cal_type == "TS":
cal_ds = cal_obj.compute_TS()
else:
raise ValueError("cal_type must be Sv or TS")
# Add attributes
def add_attrs(cal_type, ds):
"""Add attributes to backscattering strength dataset.
cal_type: Sv or TS
"""
ds["range_sample"].attrs = {"long_name": "Along-range sample number, base 0"}
ds["echo_range"].attrs = {"long_name": "Range distance", "units": "m"}
ds[cal_type].attrs = {
"long_name": {
"Sv": "Volume backscattering strength (Sv re 1 m-1)",
"TS": "Target strength (TS re 1 m^2)",
}[cal_type],
"units": "dB",
"actual_range": [
round(float(ds[cal_type].min().values), 2),
round(float(ds[cal_type].max().values), 2),
],
}
if echodata.sonar_model == "EK80":
ds[cal_type] = ds[cal_type].assign_attrs(
{
"waveform_mode": waveform_mode,
"encode_mode": encode_mode,
}
)
add_attrs(cal_type, cal_ds)
# Add provinance
# Provenance source files may originate from raw files (echodata.source_files)
# or converted files (echodata.converted_raw_path)
if echodata.source_file is not None:
source_file = echodata.source_file
elif echodata.converted_raw_path is not None:
source_file = echodata.converted_raw_path
else:
source_file = "SOURCE FILE NOT IDENTIFIED"
prov_dict = echopype_prov_attrs(process_type="processing")
prov_dict["processing_function"] = f"calibrate.compute_{cal_type}"
files_vars = source_files_vars(source_file)
cal_ds = (
cal_ds.assign(**files_vars["source_files_var"])
.assign_coords(**files_vars["source_files_coord"])
.assign_attrs(prov_dict)
)
# Add water_level to the created xr.Dataset
if "water_level" in echodata["Platform"].data_vars.keys():
cal_ds["water_level"] = echodata["Platform"].water_level
return cal_ds
def compute_Sv(echodata: EchoData, **kwargs) -> xr.Dataset:
"""
Compute volume backscattering strength (Sv) from raw data.
The calibration routine varies depending on the sonar type.
Currently this operation is supported for the following ``sonar_model``:
EK60, AZFP, EK80 (see Notes below for detail).
Parameters
----------
echodata : EchoData
An `EchoData` object created by using `open_raw` or `open_converted`
env_params : dict, optional
Environmental parameters needed for calibration.
Users can supply `"sound speed"` and `"absorption"` directly,
or specify other variables that can be used to compute them,
including `"temperature"`, `"salinity"`, and `"pressure"`.
For EK60 and EK80 echosounders, by default echopype uses
environmental variables stored in the data files.
For AZFP echosounder, all environmental parameters need to be supplied.
AZFP echosounders typically are equipped with an internal temperature
sensor, and some are equipped with a pressure sensor, but automatically
using these pressure data is not currently supported.
cal_params : dict, optional
Intrument-dependent calibration parameters.
For EK60, EK80, and AZFP echosounders, by default echopype uses
environmental variables stored in the data files.
Users can optionally pass in custom values shown below.
- for EK60 echosounder, allowed parameters include:
`"sa_correction"`, `"gain_correction"`, `"equivalent_beam_angle"`
- for AZFP echosounder, allowed parameters include:
`"EL"`, `"DS"`, `"TVR"`, `"VTX"`, `"equivalent_beam_angle"`, `"Sv_offset"`
Passing in calibration parameters for other echosounders
are not currently supported.
waveform_mode : {"CW", "BB"}, optional
Type of transmit waveform.
Required only for data from the EK80 echosounder
and not used with any other echosounder.
- `"CW"` for narrowband transmission,
returned echoes recorded either as complex or power/angle samples
- `"BB"` for broadband transmission,
returned echoes recorded as complex samples
encode_mode : {"complex", "power"}, optional
Type of encoded return echo data.
Required only for data from the EK80 echosounder
and not used with any other echosounder.
- `"complex"` for complex samples
- `"power"` for power/angle samples, only allowed when
the echosounder is configured for narrowband transmission
Returns
-------
xr.Dataset
The calibrated Sv dataset, including calibration parameters
and environmental variables used in the calibration operations.
Notes
-----
The EK80 echosounder can be configured to transmit
either broadband (``waveform_mode="BB"``)
or narrowband (``waveform_mode="CW"``) signals.
When transmitting in broadband mode, the returned echoes are
encoded as complex samples (``encode_mode="complex"``).
When transmitting in narrowband mode, the returned echoes can be encoded
either as complex samples (``encode_mode="complex"``)
or as power/angle combinations (``encode_mode="power"``) in a format
similar to those recorded by EK60 echosounders.
The current calibration implemented for EK80 broadband complex data
uses band-integrated Sv with the gain computed at the center frequency
of the transmit signal.
The returned xr.Dataset will contain the variable `water_level` from the
EchoData object provided, if it exists. If `water_level` is not returned,
it must be set using `EchoData.update_platform()`.
"""
return _compute_cal(cal_type="Sv", echodata=echodata, **kwargs)
def compute_TS(echodata: EchoData, **kwargs):
"""
Compute target strength (TS) from raw data.
The calibration routine varies depending on the sonar type.
Currently this operation is supported for the following ``sonar_model``:
EK60, AZFP, EK80 (see Notes below for detail).
Parameters
----------
echodata : EchoData
An `EchoData` object created by using `open_raw` or `open_converted`
env_params : dict, optional
Environmental parameters needed for calibration.
Users can supply `"sound speed"` and `"absorption"` directly,
or specify other variables that can be used to compute them,
including `"temperature"`, `"salinity"`, and `"pressure"`.
For EK60 and EK80 echosounders, by default echopype uses
environmental variables stored in the data files.
For AZFP echosounder, all environmental parameters need to be supplied.
AZFP echosounders typically are equipped with an internal temperature
sensor, and some are equipped with a pressure sensor, but automatically
using these pressure data is not currently supported.
cal_params : dict, optional
Intrument-dependent calibration parameters.
For EK60, EK80, and AZFP echosounders, by default echopype uses
environmental variables stored in the data files.
Users can optionally pass in custom values shown below.
- for EK60 echosounder, allowed parameters include:
`"sa_correction"`, `"gain_correction"`, `"equivalent_beam_angle"`
- for AZFP echosounder, allowed parameters include:
`"EL"`, `"DS"`, `"TVR"`, `"VTX"`, `"equivalent_beam_angle"`, `"Sv_offset"`
Passing in calibration parameters for other echosounders
are not currently supported.
waveform_mode : {"CW", "BB"}, optional
Type of transmit waveform.
Required only for data from the EK80 echosounder
and not used with any other echosounder.
- `"CW"` for narrowband transmission,
returned echoes recorded either as complex or power/angle samples
- `"BB"` for broadband transmission,
returned echoes recorded as complex samples
encode_mode : {"complex", "power"}, optional
Type of encoded return echo data.
Required only for data from the EK80 echosounder
and not used with any other echosounder.
- `"complex"` for complex samples
- `"power"` for power/angle samples, only allowed when
the echosounder is configured for narrowband transmission
Returns
-------
xr.Dataset
The calibrated TS dataset, including calibration parameters
and environmental variables used in the calibration operations.
Notes
-----
The EK80 echosounder can be configured to transmit
either broadband (``waveform_mode="BB"``)
or narrowband (``waveform_mode="CW"``) signals.
When transmitting in broadband mode, the returned echoes are
encoded as complex samples (``encode_mode="complex"``).
When transmitting in narrowband mode, the returned echoes can be encoded
either as complex samples (``encode_mode="complex"``)
or as power/angle combinations (``encode_mode="power"``) in a format
similar to those recorded by EK60 echosounders.
The current calibration implemented for EK80 broadband complex data
uses band-integrated TS with the gain computed at the center frequency
of the transmit signal.
Note that in the fisheries acoustics context, it is customary to
associate TS to a single scatterer.
TS is defined as: TS = 10 * np.log10 (sigma_bs), where sigma_bs
is the backscattering cross-section.
For details, see:
MacLennan et al. 2002. A consistent approach to definitions and
symbols in fisheries acoustics. ICES J. Mar. Sci. 59: 365-369.
https://doi.org/10.1006/jmsc.2001.1158
"""
return _compute_cal(cal_type="TS", echodata=echodata, **kwargs)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,774 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parse_ad2cp.py | from collections import OrderedDict
from enum import Enum, auto, unique
from typing import Any, BinaryIO, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from typing_extensions import Literal
from .parse_base import ParseBase
@unique
class BurstAverageDataRecordVersion(Enum):
"""
Determines the version of the burst/average data record
"""
VERSION2 = auto() # Burst/Average Data Record Definition (DF2)
VERSION3 = auto() # Burst/Average Data Record Definition (DF3)
@unique
class DataRecordType(Enum):
"""
Determines the type of data record
"""
BURST_VERSION2 = auto()
BURST_VERSION3 = auto()
AVERAGE_VERSION2 = auto()
AVERAGE_VERSION3 = auto()
ECHOSOUNDER = auto()
ECHOSOUNDER_RAW = auto()
ECHOSOUNDER_RAW_TRANSMIT = auto()
BOTTOM_TRACK = auto()
STRING = auto()
@unique
class DataType(Enum):
"""
Determines the data type of raw bytes
"""
RAW_BYTES = auto()
STRING = auto()
SIGNED_INTEGER = auto()
UNSIGNED_INTEGER = auto()
# UNSIGNED_LONG = auto()
FLOAT = auto()
SIGNED_FRACTION = auto()
def dtype(self, size_bytes: int) -> np.dtype:
if self in (SIGNED_INTEGER, UNSIGNED_INTEGER, FLOAT):
return np.dtype(DTYPES[(self, size_bytes)]) # type: ignore
elif self == RAW_BYTES:
return np.dtype("<u1")
elif self == STRING:
return np.dtype("U")
elif self == SIGNED_FRACTION:
return np.dtype("<f8")
else:
raise ValueError("unrecognized data type")
@staticmethod
def default_dtype() -> np.dtype:
return np.dtype("<u8")
RAW_BYTES = DataType.RAW_BYTES
STRING = DataType.STRING
SIGNED_INTEGER = DataType.SIGNED_INTEGER
UNSIGNED_INTEGER = DataType.UNSIGNED_INTEGER
# UNSIGNED_LONG = DataType.UNSIGNED_LONG
FLOAT = DataType.FLOAT
SIGNED_FRACTION = DataType.SIGNED_FRACTION
DtypesHint = Literal["<i1", "<i2", "<i4", "<i8", "<u1", "<u2", "<u4", "<u8", "<f2", "<f4", "<f8"]
DTYPES: Dict[Tuple[DataType, int], DtypesHint] = {
(SIGNED_INTEGER, 1): "<i1",
(SIGNED_INTEGER, 2): "<i2",
(SIGNED_INTEGER, 4): "<i4",
(SIGNED_INTEGER, 8): "<i8",
(UNSIGNED_INTEGER, 1): "<u1",
(UNSIGNED_INTEGER, 2): "<u2",
(UNSIGNED_INTEGER, 4): "<u4",
(UNSIGNED_INTEGER, 8): "<u8",
(FLOAT, 2): "<f2",
(FLOAT, 4): "<f4",
(FLOAT, 8): "<f8",
(SIGNED_FRACTION, 1): "<i1",
(SIGNED_FRACTION, 2): "<i2",
(SIGNED_FRACTION, 4): "<i4",
(SIGNED_FRACTION, 8): "<i8",
}
@unique
class Dimension(Enum):
"""
Determines the dimensions of the data in the output dataset
"""
PING_TIME = auto()
PING_TIME_AVERAGE = auto()
PING_TIME_BURST = auto()
PING_TIME_ECHOSOUNDER = auto()
PING_TIME_ECHOSOUNDER_RAW = auto()
PING_TIME_ECHOSOUNDER_RAW_TRANSMIT = auto()
BEAM = auto()
RANGE_SAMPLE_BURST = auto()
RANGE_SAMPLE_AVERAGE = auto()
RANGE_SAMPLE_ECHOSOUNDER = auto()
NUM_ALTIMETER_SAMPLES = auto()
SAMPLE = auto()
SAMPLE_TRANSMIT = auto()
MIJ = auto()
XYZ = auto()
WXYZ = auto()
def dimension_name(self) -> str:
return DIMENSION_NAMES[self]
DIMENSION_NAMES = {
Dimension.PING_TIME: "time1",
Dimension.PING_TIME_AVERAGE: "ping_time",
Dimension.PING_TIME_BURST: "ping_time",
Dimension.PING_TIME_ECHOSOUNDER: "ping_time",
Dimension.PING_TIME_ECHOSOUNDER_RAW: "ping_time",
Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT: "ping_time_transmit",
Dimension.BEAM: "beam",
Dimension.RANGE_SAMPLE_BURST: "range_sample",
Dimension.RANGE_SAMPLE_AVERAGE: "range_sample",
Dimension.RANGE_SAMPLE_ECHOSOUNDER: "range_sample",
Dimension.NUM_ALTIMETER_SAMPLES: "num_altimeter_samples",
Dimension.SAMPLE: "range_sample",
Dimension.SAMPLE_TRANSMIT: "transmit_sample",
Dimension.MIJ: "mij",
Dimension.XYZ: "xyz",
Dimension.WXYZ: "wxyz",
}
class Field:
"""
Represents a single field within a data record and controls the way
the field will be parsed
"""
def __init__(
self,
field_name: Optional[str],
field_entry_size_bytes: Union[int, Callable[["Ad2cpDataPacket"], int]],
field_entry_data_type: DataType,
# field_entry_data_type: Union[DataType, Callable[["Ad2cpDataPacket"], DataType]],
*,
field_shape: Union[List[int], Callable[["Ad2cpDataPacket"], List[int]]] = [],
field_dimensions: Union[List[Dimension], Callable[[DataRecordType], List[Dimension]]] = [
Dimension.PING_TIME
],
field_unit_conversion: Callable[
["Ad2cpDataPacket", np.ndarray], np.ndarray
] = lambda _, x: x,
field_exists_predicate: Callable[["Ad2cpDataPacket"], bool] = lambda _: True,
):
"""
field_name: Name of the field. If None, the field is parsed but ignored
field_entry_size_bytes: Size of each entry within the field, in bytes.
In most cases, the entry is the field itself, but sometimes the field
contains a list of entries.
field_entry_data_type: Data type of each entry in the field
field_shape: Shape of entries within the field.
[] (the default) means the entry is the field itself,
[n] means the field consists of a list of n entries,
[n, m] means the field consists of a two dimensional array with
n number of m length arrays,
etc.
field_dimensions: Dimensions of the field in the output dataset
field_unit_conversion: Unit conversion function on field
field_exists_predicate: Tests to see whether the field should be parsed at all
"""
self.field_name = field_name
self.field_entry_size_bytes = field_entry_size_bytes
self.field_entry_data_type = field_entry_data_type
self.field_shape = field_shape
self.field_dimensions = field_dimensions
self.field_unit_conversion = field_unit_conversion
self.field_exists_predicate = field_exists_predicate
def dimensions(self, data_record_type: DataRecordType) -> List[Dimension]:
"""
Returns the dimensions of the field given the data record type
"""
dims = self.field_dimensions
if callable(dims):
dims = dims(data_record_type)
return dims
@staticmethod
def default_dimensions() -> List[Dimension]:
"""
Returns the default dimensions for fields
"""
return [Dimension.PING_TIME]
F = Field # use F instead of Field to make the repeated fields easier to read
class NoMorePackets(Exception):
"""
Indicates that there are no more packets to be parsed from the file
"""
pass
class ParseAd2cp(ParseBase):
def __init__(self, file, params, storage_options={}, dgram_zarr_vars={}):
super().__init__(file, storage_options)
self.config = None
self.packets: List[Ad2cpDataPacket] = []
def parse_raw(self):
"""
Parses the source file into AD2CP packets
"""
with open(self.source_file, "rb") as f:
while True:
try:
packet = Ad2cpDataPacket(f, self)
self.packets.append(packet)
except NoMorePackets:
break
else:
if self.config is None and packet.is_string():
self.config = self.parse_config(packet.data["string_data"])
if self.config is not None and "GETCLOCKSTR" in self.config:
self.ping_time.append(np.datetime64(self.config["GETCLOCKSTR"]["TIME"]))
else:
self.ping_time.append(np.datetime64())
@staticmethod
def parse_config(data: np.ndarray) -> Dict[str, Dict[str, Any]]:
"""
Parses the configuration string for the ADCP, which will be the first string data record.
The data is in the form:
HEADING1,KEY1=VALUE1,KEY2=VALUE2
HEADING2,KEY3=VALUE3,KEY4=VALUE4,KEY5=VALUE5
...
where VALUEs can be
strings: "foo"
integers: 123
floats: 123.456
"""
result = dict()
for line in data[()].splitlines():
tokens = line.split(",")
line_dict = dict()
for token in tokens[1:]:
k, v = token.split("=")
if v.startswith('"'):
v = v.strip('"')
else:
try:
v = int(v)
except ValueError:
try:
v = float(v)
except ValueError:
v = str(v)
line_dict[k] = v
result[tokens[0]] = line_dict
return result
def get_firmware_version(self) -> Optional[Dict[str, Any]]:
return self.config.get("GETHW") # type: ignore
def get_pulse_compressed(self) -> int:
for i in range(1, 3 + 1):
if "GETECHO" in self.config and self.config["GETECHO"][f"PULSECOMP{i}"] > 0: # type: ignore # noqa
return i
return 0
class Ad2cpDataPacket:
"""
Represents a single data packet. Each data packet consists of a header data record followed by a
"""
def __init__(
self,
f: BinaryIO,
parser: ParseAd2cp,
):
self.parser = parser
self.data_record_type: Optional[DataRecordType] = None
self.data = dict()
self._read_header(f)
self._read_data_record(f)
@property
def timestamp(self) -> np.datetime64:
"""
Calculates and returns the timestamp of the packet
"""
year = self.data["year"] + 1900
month = self.data["month"] + 1
day = self.data["day"]
hour = self.data["hour"]
minute = self.data["minute"]
seconds = self.data["seconds"]
microsec100 = self.data["microsec100"]
try:
return np.datetime64(
f"{year:04}-{month:02}-{day:02}T{hour:02}:{minute:02}:{seconds:02}.{microsec100:04}"
) # type: ignore
except ValueError:
return np.datetime64("NaT") # type: ignore
def is_burst(self) -> bool:
"""
Returns whether the current packet is a burst packet
"""
return self.data["id"] in (0x15, 0x18)
def is_average(self) -> bool:
"""
Returns whether the current packet is an average packet
"""
return self.data["id"] == 0x16
def is_bottom_track(self) -> bool:
"""
Returns whether the current packet is a bottom track packet
"""
return self.data["id"] in (0x17, 0x1B)
def is_echosounder(self) -> bool:
"""
Returns whether the current packet is an echosounder packet
"""
return self.data["id"] == 0x1C
def is_echosounder_raw(self) -> bool:
"""
Returns whether the current packet is a raw echosounder packet
"""
return self.data["id"] == 0x23
def is_echosounder_raw_transmit(self) -> bool:
"""
Returns whether the current packet is a raw echosounder transmit packet
"""
return self.data["id"] == 0x24
def is_burst_altimeter(self) -> bool:
return self.data["id"] == 0x1A
def is_dvl_water_track(self) -> bool:
return self.data["id"] == 0x1D
def is_altimeter(self) -> bool:
return self.data["id"] == 0x1E
def is_average_altimeter(self) -> bool:
return self.data["id"] == 0x1F
def is_string(self) -> bool:
"""
Returns whether the current packet is a string packet
"""
return self.data["id"] == 0xA0
def has_timestamp(self) -> bool:
"""
Returns whether the packet has a timestamp (.timestamp can be called)
"""
return not self.is_string()
def _read_header(self, f: BinaryIO):
"""
Reads the header part of the AD2CP packet from the given stream
"""
self.data_record_format = HeaderOrDataRecordFormats.HEADER_FORMAT
raw_header = self._read_data(f, self.data_record_format)
# don't include the last 2 bytes, which is the header checksum itself
calculated_checksum = self.checksum(raw_header[:-2])
expected_checksum = self.data["header_checksum"]
assert (
calculated_checksum == expected_checksum
), f"invalid header checksum: found {calculated_checksum}, expected {expected_checksum}"
def _read_data_record(self, f: BinaryIO):
"""
Reads the data record part of the AD2CP packet from the stream
"""
if self.is_burst(): # burst
self.data_record_type = DataRecordType.BURST_VERSION3
elif self.is_average(): # average
self.data_record_type = DataRecordType.AVERAGE_VERSION3
elif self.is_bottom_track(): # bottom track
self.data_record_type = DataRecordType.BOTTOM_TRACK
elif self.is_echosounder_raw(): # echosounder raw
self.data_record_type = DataRecordType.ECHOSOUNDER_RAW
elif self.is_echosounder_raw_transmit(): # echosounder raw transmit
self.data_record_type = DataRecordType.ECHOSOUNDER_RAW_TRANSMIT
elif self.is_burst_altimeter(): # burst altimeter
# altimeter is only supported by burst/average version 3
self.data_record_type = DataRecordType.BURST_VERSION3
elif self.is_echosounder(): # echosounder
# echosounder is only supported by burst/average version 3
self.data_record_type = DataRecordType.ECHOSOUNDER
elif self.is_dvl_water_track(): # dvl water track record
# TODO: is this correct?
self.data_record_type = DataRecordType.AVERAGE_VERSION3
elif self.is_altimeter(): # altimeter
# altimeter is only supported by burst/average version 3
self.data_record_type = DataRecordType.AVERAGE_VERSION3
elif self.is_average_altimeter(): # average altimeter
self.data_record_type = DataRecordType.AVERAGE_VERSION3
elif self.is_string(): # string data
self.data_record_type = DataRecordType.STRING
else:
raise ValueError("invalid data record type id: 0x{:02x}".format(self.data["id"]))
self.data_record_format = HeaderOrDataRecordFormats.data_record_format(
self.data_record_type
)
raw_data_record = self._read_data(f, self.data_record_format)
calculated_checksum = self.checksum(raw_data_record)
expected_checksum = self.data["data_record_checksum"]
assert (
calculated_checksum == expected_checksum
), f"invalid data record checksum: found {calculated_checksum}, expected {expected_checksum}" # noqa
def _read_data(self, f: BinaryIO, data_format: "HeaderOrDataRecordFormat") -> bytes:
"""
Reads data from the stream, interpreting the data using the given format
"""
raw_bytes = bytes() # combination of all raw fields
for field_format in data_format.fields_iter():
field_name = field_format.field_name
field_entry_size_bytes = field_format.field_entry_size_bytes
field_entry_data_type = field_format.field_entry_data_type
field_shape = field_format.field_shape
field_unit_conversion = field_format.field_unit_conversion
field_exists_predicate = field_format.field_exists_predicate
if not field_exists_predicate(self):
continue
if callable(field_entry_size_bytes):
field_entry_size_bytes = field_entry_size_bytes(self)
# if callable(field_entry_data_type):
# field_entry_data_type = field_entry_data_type(self)
if callable(field_shape):
field_shape = field_shape(self)
raw_field = self._read_exact(f, field_entry_size_bytes * int(np.prod(field_shape)))
raw_bytes += raw_field
# we cannot check for this before reading because some fields are placeholder fields
# which, if not read in the correct order with other fields,
# will offset the rest of the data
if field_name is not None:
parsed_field = self._parse(raw_field, field_entry_data_type, field_entry_size_bytes)
parsed_field = np.reshape(parsed_field, field_shape)
parsed_field = field_unit_conversion(self, parsed_field)
self.data[field_name] = parsed_field
self._postprocess(field_name)
return raw_bytes
@staticmethod
def _parse(value: bytes, data_type: DataType, size_bytes: int) -> np.ndarray:
"""
Parses raw bytes into a value given its data type
"""
if data_type in (SIGNED_INTEGER, UNSIGNED_INTEGER, FLOAT):
dtype = np.dtype(DTYPES[(data_type, size_bytes)]) # type: ignore
return np.frombuffer(value, dtype=dtype)
elif data_type == RAW_BYTES:
return np.frombuffer(value, dtype="<u1")
elif data_type == STRING:
return np.array(value.decode("utf-8"))
elif data_type == SIGNED_FRACTION:
# Although the specification states that the data is represented in a
# signed-magnitude format, an email exchange with Nortek revealed that it is
# actually in 2's complement form.
dtype = np.dtype(DTYPES[(SIGNED_FRACTION, size_bytes)]) # type: ignore
return (np.frombuffer(value, dtype=dtype) / (np.iinfo(dtype).max + 1)).astype("<f8")
else:
raise ValueError("unrecognized data type")
@staticmethod
def _read_exact(f: BinaryIO, total_num_bytes_to_read: int) -> bytes:
"""
Drives a stream until an exact amount of bytes is read from it.
This is necessary because a single read may not return the correct number of bytes
(see https://github.com/python/cpython/blob/5e437fb872279960992c9a07f1a4c051b4948c53/Python/fileutils.c#L1599-L1661
and https://github.com/python/cpython/blob/63298930fb531ba2bb4f23bc3b915dbf1e17e9e1/Modules/_io/fileio.c#L778-L835,
note "Only makes one system call, so less data may be returned than requested")
(see https://man7.org/linux/man-pages/man2/read.2.html#RETURN_VALUE,
note "It is not an error if this number is smaller than the number of bytes requested")
(see https://docs.microsoft.com/en-us/cpp/c-runtime-library/reference/read?view=msvc-160#return-value,
note "_read returns the number of bytes read,
which might be less than buffer_size...if the file was opened in text mode")
""" # noqa
all_bytes_read = bytes()
if total_num_bytes_to_read <= 0:
return all_bytes_read
last_bytes_read = None
while last_bytes_read is None or (
len(last_bytes_read) > 0 and len(all_bytes_read) < total_num_bytes_to_read
):
last_bytes_read = f.read(total_num_bytes_to_read - len(all_bytes_read))
if len(last_bytes_read) == 0:
# 0 bytes read with non-0 bytes requested means eof
raise NoMorePackets
else:
all_bytes_read += last_bytes_read
return all_bytes_read
def _postprocess_bitfield(
self,
field_value: np.ndarray,
bitfield_format: List[Tuple[str, int, int]],
):
"""
_postprocess helper; postprocesses a bitfield
bitfield_format:
[
(bit sequence name, start bit, end bit)
]
e.g., with mask 0b00111100, start bit is 5 and end bit is 2
"""
for bit_sequence_name, start_bit, end_bit in bitfield_format:
self.data[bit_sequence_name] = np.array(
(field_value >> end_bit) & ((1 << (start_bit - end_bit + 1)) - 1),
dtype="<u8",
)
def _postprocess_beams(
self,
field_value: np.ndarray,
beams_format: List[Tuple[int, int]],
):
"""
_postprocess helper; postprocesses beams
beams_format:
[
(start bit, end bit)
]
"""
beams = []
for start_bit, end_bit in beams_format:
beam = (field_value >> end_bit) & ((1 << (start_bit - end_bit + 1)) - 1)
if beam > 0:
beams.append(beam)
self.data["beams"] = np.array(beams, dtype="<u8")
def _postprocess(self, field_name):
"""
Calculates values based on parsed data. This should be called immediately after
parsing each field in a data record.
"""
if (
self.data_record_format
== HeaderOrDataRecordFormats.BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT
):
if field_name == "version":
if self.data["version"] == 3:
self.data_record_format = (
HeaderOrDataRecordFormats.BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT
)
elif field_name == "configuration":
self._postprocess_bitfield(
self.data["configuration"],
[
("pressure_sensor_valid", 0, 0),
("temperature_sensor_valid", 1, 1),
("compass_sensor_valid", 2, 2),
("tilt_sensor_valid", 3, 3),
("velocity_data_included", 5, 5),
("amplitude_data_included", 6, 6),
("correlation_data_included", 7, 7),
],
)
elif field_name == "num_beams_and_coordinate_system_and_num_cells":
self._postprocess_bitfield(
self.data["num_beams_and_coordinate_system_and_num_cells"],
[
("num_cells", 9, 0),
("coordinate_system", 11, 10),
("num_beams", 15, 12),
],
)
elif field_name == "dataset_description":
self._postprocess_beams(
self.data["dataset_description"],
[(2, 0), (5, 3), (8, 6), (11, 9), (14, 12)],
)
elif (
self.data_record_format
== HeaderOrDataRecordFormats.BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT
):
if field_name == "version":
if self.data["version"] == 2:
self.data_record_format = (
HeaderOrDataRecordFormats.BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT
)
elif field_name == "configuration":
self._postprocess_bitfield(
self.data["configuration"],
[
("pressure_sensor_valid", 0, 0),
("temperature_sensor_valid", 1, 1),
("compass_sensor_valid", 2, 2),
("tilt_sensor_valid", 3, 3),
("velocity_data_included", 5, 5),
("amplitude_data_included", 6, 6),
("correlation_data_included", 7, 7),
("altimeter_data_included", 8, 8),
("altimeter_raw_data_included", 9, 9),
("ast_data_included", 10, 10),
("echosounder_data_included", 11, 11),
("ahrs_data_included", 12, 12),
("percentage_good_data_included", 13, 13),
("std_dev_data_included", 14, 14),
],
)
elif field_name == "num_beams_and_coordinate_system_and_num_cells":
if self.data["echosounder_data_included"]:
self.data["num_echosounder_cells"] = self.data[
"num_beams_and_coordinate_system_and_num_cells"
]
else:
self._postprocess_bitfield(
self.data["num_beams_and_coordinate_system_and_num_cells"],
[
("num_cells", 9, 0),
("coordinate_system", 11, 10),
("num_beams", 15, 12),
],
)
elif field_name == "ambiguity_velocity_or_echosounder_frequency":
if self.data["echosounder_data_included"]:
# This is specified as "echo sounder frequency", but the description technically
# says "number of echo sounder cells".
# It is probably the frequency and not the number of cells
# because the number of cells already replaces the data in
# "num_beams_and_coordinate_system_and_num_cells"
# when an echo sounder is present
self.data["echosounder_frequency"] = self.data[
"ambiguity_velocity_or_echosounder_frequency"
]
else:
self.data["ambiguity_velocity"] = self.data[
"ambiguity_velocity_or_echosounder_frequency"
]
elif field_name == "velocity_scaling":
if not self.data["echosounder_data_included"]:
# The unit conversion for ambiguity velocity is done here because it
# requires the velocity_scaling, which is not known
# when ambiguity velocity field is parsed
self.data["ambiguity_velocity"] = self.data["ambiguity_velocity"] * (
10.0 ** self.data["velocity_scaling"]
)
elif field_name == "dataset_description":
self._postprocess_beams(
self.data["dataset_description"],
[(3, 0), (7, 4), (11, 8), (16, 12)],
)
if (
self.parser.packets[-1].is_echosounder_raw()
or self.parser.packets[-1].is_echosounder_raw_transmit()
):
self.parser.packets[-1].data["echosounder_raw_beam"] = self.data["beams"][0]
elif field_name == "status0":
if self.data["status0"] & 0b1000_0000_0000_0000:
self._postprocess_bitfield(
self.data["status0"],
[
("procidle3", 0, 0),
("procidle6", 1, 1),
("procidle12", 2, 2),
],
)
elif field_name == "status":
self._postprocess_bitfield(
self.data["status"],
[
("wakeup_state", 31, 28),
("orientation", 27, 25),
("autoorientation", 24, 22),
("previous_wakeup_state", 21, 18),
("last_measurement_low_voltage_skip", 17, 17),
("active_configuration", 16, 16),
("echosounder_index", 15, 12),
("telemetry_data", 11, 11),
("boost_running", 10, 10),
("echosounder_frequency_bin", 9, 5),
("bd_scaling", 1, 1),
],
)
elif self.data_record_format == HeaderOrDataRecordFormats.BOTTOM_TRACK_DATA_RECORD_FORMAT:
if field_name == "configuration":
self._postprocess_bitfield(
self.data["configuration"],
[
("pressure_sensor_valid", 0, 0),
("temperature_sensor_valid", 1, 1),
("compass_sensor_valid", 2, 2),
("tilt_sensor_valid", 3, 3),
("velocity_data_included", 5, 5),
("distance_data_included", 8, 8),
("figure_of_merit_data_included", 9, 9),
("ahrs_data_included", 10, 10),
],
)
elif field_name == "num_beams_and_coordinate_system_and_num_cells":
self._postprocess_bitfield(
self.data["num_beams_and_coordinate_system_and_num_cells"],
[
("num_cells", 9, 0),
("coordinate_system", 11, 10),
("num_beams", 15, 12),
],
)
elif field_name == "dataset_description":
self._postprocess_beams(
self.data["dataset_description"],
[(16, 12), (11, 8), (7, 4), (3, 0)],
)
elif field_name == "velocity_scaling":
# The unit conversion for ambiguity velocity is done here because it
# requires the velocity_scaling,
# which is not known when ambiguity velocity field is parsed
self.data["ambiguity_velocity"] = self.data["ambiguity_velocity"] * (
10.0 ** self.data["velocity_scaling"]
)
elif (
self.data_record_format == HeaderOrDataRecordFormats.ECHOSOUNDER_RAW_DATA_RECORD_FORMAT
):
if field_name == "echosounder_raw_samples":
self.data["echosounder_raw_samples_i"] = self.data["echosounder_raw_samples"][:, 0]
self.data["echosounder_raw_samples_q"] = self.data["echosounder_raw_samples"][:, 1]
elif field_name == "echosounder_raw_transmit_samples":
self.data["echosounder_raw_transmit_samples_i"] = self.data[
"echosounder_raw_transmit_samples"
][:, 0]
self.data["echosounder_raw_transmit_samples_q"] = self.data[
"echosounder_raw_transmit_samples"
][:, 1]
elif field_name == "status":
self._postprocess_bitfield(
self.data["status"],
[
("wakeup_state", 31, 28),
("orientation", 27, 25),
("autoorientation", 24, 22),
("previous_wakeup_state", 21, 18),
("last_measurement_low_voltage_skip", 17, 17),
("active_configuration", 16, 16),
("echosounder_index", 15, 12),
("telemetry_data", 11, 11),
("boost_running", 10, 10),
("echosounder_frequency_bin", 9, 5),
("bd_scaling", 1, 1),
],
)
@staticmethod
def checksum(data: bytes) -> int:
"""
Computes the checksum for the given data
"""
checksum = 0xB58C
for i in range(0, len(data), 2):
checksum += int.from_bytes(data[i : i + 2], byteorder="little")
checksum %= 2**16
if len(data) % 2 == 1:
checksum += data[-1] << 8
checksum %= 2**16
return checksum
RANGE_SAMPLES = {
DataRecordType.AVERAGE_VERSION2: Dimension.RANGE_SAMPLE_AVERAGE,
DataRecordType.AVERAGE_VERSION3: Dimension.RANGE_SAMPLE_AVERAGE,
DataRecordType.BURST_VERSION2: Dimension.RANGE_SAMPLE_BURST,
DataRecordType.BURST_VERSION3: Dimension.RANGE_SAMPLE_BURST,
DataRecordType.ECHOSOUNDER: Dimension.RANGE_SAMPLE_ECHOSOUNDER,
}
class HeaderOrDataRecordFormat:
"""
A collection of fields which represents the header format or a data record format
"""
def __init__(self, name: str, fields: List[Field]):
self.name = name
self.fields = OrderedDict([(f.field_name, f) for f in fields])
def get_field(self, field_name: str) -> Optional[Field]:
"""
Gets a field from the current packet based on its name.
Since the field could also be in the packet's header, the header
is searched in addition to this data record.
"""
if field_name in HeaderOrDataRecordFormats.HEADER_FORMAT.fields:
return HeaderOrDataRecordFormats.HEADER_FORMAT.fields.get(field_name)
return self.fields.get(field_name)
def fields_iter(self) -> Iterable[Field]:
"""
Returns an iterable over the fields in this header or data record format
"""
return self.fields.values()
class HeaderOrDataRecordFormats:
@classmethod
def data_record_format(cls, data_record_type: DataRecordType) -> HeaderOrDataRecordFormat:
"""
Returns data record format that should be used to parse the given data record type
"""
return cls.DATA_RECORD_FORMATS[data_record_type]
HEADER_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"HEADER_FORMAT",
[
F("sync", 1, UNSIGNED_INTEGER),
F("header_size", 1, UNSIGNED_INTEGER),
F("id", 1, UNSIGNED_INTEGER),
F("family", 1, UNSIGNED_INTEGER),
F(
"data_record_size",
lambda packet: 4 if packet.data["id"] in (0x23, 0x24) else 2,
UNSIGNED_INTEGER,
),
# F("data_record_size", lambda packet: 4 if packet.raw_fields["id"] in (
# 0x23, 0x24) else 2, lambda packet: UNSIGNED_LONG if packet.raw_fields["id"]
# in (0x23, 0x24) else UNSIGNED_INTEGER),
F("data_record_checksum", 2, UNSIGNED_INTEGER),
F("header_checksum", 2, UNSIGNED_INTEGER),
],
)
STRING_DATA_RECORD_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"STRING_DATA_RECORD_FORMAT",
[
F("string_data_id", 1, UNSIGNED_INTEGER),
F(
"string_data",
lambda packet: packet.data["data_record_size"] - 1,
STRING,
),
],
)
BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT",
[
F("version", 1, UNSIGNED_INTEGER),
F("offset_of_data", 1, UNSIGNED_INTEGER),
F("serial_number", 4, UNSIGNED_INTEGER),
F("configuration", 2, UNSIGNED_INTEGER),
F("year", 1, UNSIGNED_INTEGER),
F("month", 1, UNSIGNED_INTEGER),
F("day", 1, UNSIGNED_INTEGER),
F("hour", 1, UNSIGNED_INTEGER),
F("minute", 1, UNSIGNED_INTEGER),
F("seconds", 1, UNSIGNED_INTEGER),
F("microsec100", 2, UNSIGNED_INTEGER),
F(
"speed_of_sound",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pressure",
4,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"heading",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pitch",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"roll",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F("error", 2, UNSIGNED_INTEGER),
F("status", 2, UNSIGNED_INTEGER),
F("num_beams_and_coordinate_system_and_num_cells", 2, UNSIGNED_INTEGER),
F(
"cell_size",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"blanking",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"velocity_range",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"battery_voltage",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"magnetometer_raw",
2,
SIGNED_INTEGER,
field_shape=[3],
field_dimensions=[Dimension.PING_TIME, Dimension.XYZ],
),
F(
"accelerometer_raw_x_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_y_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_z_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"ambiguity_velocity",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10000,
),
F("dataset_description", 2, UNSIGNED_INTEGER),
F("transmit_energy", 2, UNSIGNED_INTEGER),
F("velocity_scaling", 1, SIGNED_INTEGER),
F("power_level", 1, SIGNED_INTEGER),
F(None, 4, UNSIGNED_INTEGER),
F( # used when burst
"velocity_data_burst",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["velocity_data_included"],
),
F( # used when average
"velocity_data_average",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["velocity_data_included"],
),
F( # used when echosounder
"velocity_data_echosounder",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["velocity_data_included"],
),
F(
"amplitude_data_burst",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["amplitude_data_included"],
),
F(
"amplitude_data_average",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["amplitude_data_included"],
),
F(
"amplitude_data_echosounder",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["amplitude_data_included"],
),
F(
"correlation_data_burst",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["correlation_data_included"],
),
F(
"correlation_data_average",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["correlation_data_included"],
),
F(
"correlation_data_echosounder",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["correlation_data_included"],
),
],
)
BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT",
[
F("version", 1, UNSIGNED_INTEGER),
F("offset_of_data", 1, UNSIGNED_INTEGER),
F("configuration", 2, UNSIGNED_INTEGER),
F("serial_number", 4, UNSIGNED_INTEGER),
F("year", 1, UNSIGNED_INTEGER),
F("month", 1, UNSIGNED_INTEGER),
F("day", 1, UNSIGNED_INTEGER),
F("hour", 1, UNSIGNED_INTEGER),
F("minute", 1, UNSIGNED_INTEGER),
F("seconds", 1, UNSIGNED_INTEGER),
F("microsec100", 2, UNSIGNED_INTEGER),
F(
"speed_of_sound",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pressure",
4,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"heading",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pitch",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"roll",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F("num_beams_and_coordinate_system_and_num_cells", 2, UNSIGNED_INTEGER),
F(
"cell_size",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
# This field is listed to be in cm, but testing has shown that it is actually in mm.
# Being in mm would be consistent with the "blanking" field units in all other formats.
F(
"blanking",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F("nominal_correlation", 1, UNSIGNED_INTEGER),
F(
"temperature_from_pressure_sensor",
1,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x * 5,
),
F(
"battery_voltage",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"magnetometer_raw",
2,
SIGNED_INTEGER,
field_shape=[3],
field_dimensions=[Dimension.PING_TIME, Dimension.XYZ],
),
F(
"accelerometer_raw_x_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_y_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_z_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
# Unit conversions for this field are done in Ad2cpDataPacket._postprocess
# because the ambiguity velocity unit conversion requires the velocity_scaling field,
# which is not known when this field is parsed
F("ambiguity_velocity_or_echosounder_frequency", 2, UNSIGNED_INTEGER),
F("dataset_description", 2, UNSIGNED_INTEGER),
F("transmit_energy", 2, UNSIGNED_INTEGER),
F("velocity_scaling", 1, SIGNED_INTEGER),
F("power_level", 1, SIGNED_INTEGER),
F(
"magnetometer_temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x * 1000,
),
F(
"real_time_clock_temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F("error", 2, UNSIGNED_INTEGER),
F("status0", 2, UNSIGNED_INTEGER),
F("status", 4, UNSIGNED_INTEGER),
F("ensemble_counter", 4, UNSIGNED_INTEGER),
F(
"velocity_data_burst",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["velocity_data_included"],
),
F(
"velocity_data_average",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["velocity_data_included"],
),
F(
"velocity_data_echosounder",
2,
SIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["velocity_data_included"],
),
F(
"amplitude_data_burst",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["amplitude_data_included"],
),
F(
"amplitude_data_average",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["amplitude_data_included"],
),
F(
"amplitude_data_echosounder",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_unit_conversion=lambda packet, x: x / 2,
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["amplitude_data_included"],
),
F(
"correlation_data_burst",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_BURST,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_BURST,
],
field_exists_predicate=lambda packet: packet.is_burst()
and packet.data["correlation_data_included"],
),
F(
"correlation_data_average",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_AVERAGE,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_AVERAGE,
],
field_exists_predicate=lambda packet: packet.is_average()
and packet.data["correlation_data_included"],
),
F(
"correlation_data_echosounder",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [
packet.data.get("num_beams", 0),
packet.data.get("num_cells", 0),
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.BEAM,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_exists_predicate=lambda packet: packet.is_echosounder()
and packet.data["correlation_data_included"],
),
F(
"altimeter_distance",
4,
FLOAT,
field_exists_predicate=lambda packet: packet.data["altimeter_data_included"],
),
F(
"altimeter_quality",
2,
UNSIGNED_INTEGER,
field_exists_predicate=lambda packet: packet.data["altimeter_data_included"],
),
F(
"ast_distance",
4,
FLOAT,
field_exists_predicate=lambda packet: packet.data["ast_data_included"],
),
F(
"ast_quality",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["ast_data_included"],
),
F(
"ast_offset_100us",
2,
SIGNED_INTEGER,
field_exists_predicate=lambda packet: packet.data["ast_data_included"],
),
F(
"ast_pressure",
4,
FLOAT,
field_exists_predicate=lambda packet: packet.data["ast_data_included"],
),
F(
"altimeter_spare",
1,
RAW_BYTES,
field_shape=[8],
field_exists_predicate=lambda packet: packet.data["ast_data_included"],
),
F(
"altimeter_raw_data_num_samples",
# The field size of this field is technically specified as number of samples * 2,
# but seeing as the field is called "num samples," and the field which is supposed
# to contain the samples is specified as having a constant size of 2, these fields
# sizes were likely incorrectly swapped.
2,
UNSIGNED_INTEGER,
field_exists_predicate=lambda packet: packet.data["altimeter_raw_data_included"],
),
F(
"altimeter_raw_data_sample_distance",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10000,
field_exists_predicate=lambda packet: packet.data["altimeter_raw_data_included"],
),
F(
"altimeter_raw_data_samples",
2,
SIGNED_FRACTION,
field_shape=lambda packet: [packet.data["altimeter_raw_data_num_samples"]],
field_dimensions=[Dimension.PING_TIME, Dimension.NUM_ALTIMETER_SAMPLES],
field_exists_predicate=lambda packet: packet.data["altimeter_raw_data_included"],
),
F(
"echosounder_data",
2,
# Although the specification says that this should be an unsigned integer,
# testing has shown that it should be a signed integer
SIGNED_INTEGER,
field_shape=lambda packet: [packet.data.get("num_echosounder_cells", 0)],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER,
Dimension.RANGE_SAMPLE_ECHOSOUNDER,
],
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["echosounder_data_included"],
),
F(
"ahrs_rotation_matrix",
4,
FLOAT,
field_shape=[9],
field_dimensions=[Dimension.PING_TIME, Dimension.MIJ],
field_exists_predicate=lambda packet: packet.data["ahrs_data_included"],
),
F(
"ahrs_quaternions",
4,
FLOAT,
field_shape=[4],
field_dimensions=[Dimension.PING_TIME, Dimension.WXYZ],
field_exists_predicate=lambda packet: packet.data["ahrs_data_included"],
),
F(
"ahrs_gyro",
4,
FLOAT,
field_shape=[3],
field_dimensions=[Dimension.PING_TIME, Dimension.XYZ],
field_exists_predicate=lambda packet: packet.data["ahrs_data_included"],
),
F(
"percentage_good_data",
1,
UNSIGNED_INTEGER,
field_shape=lambda packet: [packet.data.get("num_cells", 0)],
field_dimensions=lambda data_record_type: [
Dimension.PING_TIME,
RANGE_SAMPLES[data_record_type],
],
field_exists_predicate=lambda packet: packet.data["percentage_good_data_included"],
),
# Only the pitch field is labeled as included when the "std dev data included"
# bit is set, but this is likely a mistake
F(
"std_dev_pitch",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["std_dev_data_included"],
),
F(
"std_dev_roll",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["std_dev_data_included"],
),
F(
"std_dev_heading",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["std_dev_data_included"],
),
F(
"std_dev_pressure",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
field_exists_predicate=lambda packet: packet.data["std_dev_data_included"],
),
F(
None,
24,
RAW_BYTES,
field_exists_predicate=lambda packet: packet.data["std_dev_data_included"],
),
],
)
BOTTOM_TRACK_DATA_RECORD_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"BOTTOM_TRACK_DATA_RECORD_FORMAT",
[
F("version", 1, UNSIGNED_INTEGER),
F("offset_of_data", 1, UNSIGNED_INTEGER),
F("configuration", 2, UNSIGNED_INTEGER),
F("serial_number", 4, UNSIGNED_INTEGER),
F("year", 1, UNSIGNED_INTEGER),
F("month", 1, UNSIGNED_INTEGER),
F("day", 1, UNSIGNED_INTEGER),
F("hour", 1, UNSIGNED_INTEGER),
F("minute", 1, UNSIGNED_INTEGER),
F("seconds", 1, UNSIGNED_INTEGER),
F("microsec100", 2, UNSIGNED_INTEGER),
F(
"speed_of_sound",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pressure",
4,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"heading",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"pitch",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F(
"roll",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F("num_beams_and_coordinate_system_and_num_cells", 2, UNSIGNED_INTEGER),
F(
"cell_size",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F(
"blanking",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 1000,
),
F("nominal_correlation", 1, UNSIGNED_INTEGER),
F(None, 1, RAW_BYTES),
F(
"battery_voltage",
2,
UNSIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 10,
),
F(
"magnetometer_raw",
2,
SIGNED_INTEGER,
field_shape=[3],
field_dimensions=[Dimension.PING_TIME, Dimension.XYZ],
),
F(
"accelerometer_raw_x_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_y_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
F(
"accelerometer_raw_z_axis",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 16384 * 9.819,
),
# Unit conversions for this field are done in Ad2cpDataPacket._postprocess
# because the ambiguity velocity unit conversion requires the velocity_scaling field,
# which is not known when this field is parsed
F("ambiguity_velocity", 4, UNSIGNED_INTEGER),
F("dataset_description", 2, UNSIGNED_INTEGER),
F("transmit_energy", 2, UNSIGNED_INTEGER),
F("velocity_scaling", 1, SIGNED_INTEGER),
F("power_level", 1, SIGNED_INTEGER),
F(
"magnetometer_temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x * 1000,
),
F(
"real_time_clock_temperature",
2,
SIGNED_INTEGER,
field_unit_conversion=lambda packet, x: x / 100,
),
F("error", 4, UNSIGNED_INTEGER),
F("status", 4, UNSIGNED_INTEGER),
F("ensemble_counter", 4, UNSIGNED_INTEGER),
F(
"velocity_data",
4,
SIGNED_INTEGER,
field_shape=lambda packet: [packet.data.get("num_beams", 0)],
field_dimensions=[Dimension.PING_TIME, Dimension.BEAM],
field_unit_conversion=lambda packet, x: x
* (10.0 ** packet.data["velocity_scaling"]),
field_exists_predicate=lambda packet: packet.data["velocity_data_included"],
),
F(
"distance_data",
4,
SIGNED_INTEGER,
field_shape=lambda packet: [packet.data.get("num_beams", 0)],
field_dimensions=[Dimension.PING_TIME, Dimension.BEAM],
field_unit_conversion=lambda packet, x: x / 1000,
field_exists_predicate=lambda packet: packet.data["distance_data_included"],
),
F(
"figure_of_merit_data",
2,
UNSIGNED_INTEGER,
field_shape=lambda packet: [packet.data.get("num_beams", 0)],
field_dimensions=[Dimension.PING_TIME, Dimension.BEAM],
field_exists_predicate=lambda packet: packet.data["figure_of_merit_data_included"],
),
],
)
ECHOSOUNDER_RAW_DATA_RECORD_FORMAT: HeaderOrDataRecordFormat = HeaderOrDataRecordFormat(
"ECHOSOUNDER_RAW_DATA_RECORD_FORMAT",
[
F("version", 1, UNSIGNED_INTEGER),
F("offset_of_data", 1, UNSIGNED_INTEGER),
F("year", 1, UNSIGNED_INTEGER),
F("month", 1, UNSIGNED_INTEGER),
F("day", 1, UNSIGNED_INTEGER),
F("hour", 1, UNSIGNED_INTEGER),
F("minute", 1, UNSIGNED_INTEGER),
F("seconds", 1, UNSIGNED_INTEGER),
F("microsec100", 2, UNSIGNED_INTEGER),
F("error", 2, UNSIGNED_INTEGER),
F("status", 4, UNSIGNED_INTEGER),
F("serial_number", 4, UNSIGNED_INTEGER),
F("num_complex_samples", 4, UNSIGNED_INTEGER),
F("ind_start_samples", 4, UNSIGNED_INTEGER),
F("freq_raw_sample_data", 4, FLOAT),
F(None, 208, RAW_BYTES),
F(
"echosounder_raw_samples",
4,
SIGNED_FRACTION,
field_shape=lambda packet: [
packet.data["num_complex_samples"],
2,
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW,
Dimension.SAMPLE,
],
field_exists_predicate=lambda packet: packet.is_echosounder_raw(),
),
# These next 2 fields are included so that the dimensions for these fields
# can be determined based on the field name.
# They are actually constructed in _postprocess.
F(
"echosounder_raw_samples_i",
0,
RAW_BYTES,
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW,
Dimension.SAMPLE,
],
field_exists_predicate=lambda packet: False,
),
F(
"echosounder_raw_samples_q",
0,
RAW_BYTES,
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW,
Dimension.SAMPLE,
],
field_exists_predicate=lambda packet: False,
),
F(
"echosounder_raw_transmit_samples",
4,
SIGNED_FRACTION,
field_shape=lambda packet: [
packet.data["num_complex_samples"],
2,
],
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT,
Dimension.SAMPLE_TRANSMIT,
],
field_exists_predicate=lambda packet: packet.is_echosounder_raw_transmit(),
),
# These next 2 fields are included so that the dimensions for these fields
# can be determined based on the field name.
# They are actually constructed in _postprocess.
F(
"echosounder_raw_transmit_samples_i",
0,
RAW_BYTES,
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT,
Dimension.SAMPLE_TRANSMIT,
],
field_exists_predicate=lambda packet: False,
),
F(
"echosounder_raw_transmit_samples_q",
0,
RAW_BYTES,
field_dimensions=[
Dimension.PING_TIME_ECHOSOUNDER_RAW_TRANSMIT,
Dimension.SAMPLE_TRANSMIT,
],
field_exists_predicate=lambda packet: False,
),
],
)
DATA_RECORD_FORMATS = {
DataRecordType.BURST_VERSION2: BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT,
DataRecordType.BURST_VERSION3: BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT,
DataRecordType.AVERAGE_VERSION2: BURST_AVERAGE_VERSION2_DATA_RECORD_FORMAT,
DataRecordType.AVERAGE_VERSION3: BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT,
DataRecordType.BOTTOM_TRACK: BOTTOM_TRACK_DATA_RECORD_FORMAT,
DataRecordType.ECHOSOUNDER: BURST_AVERAGE_VERSION3_DATA_RECORD_FORMAT,
DataRecordType.ECHOSOUNDER_RAW: ECHOSOUNDER_RAW_DATA_RECORD_FORMAT,
DataRecordType.ECHOSOUNDER_RAW_TRANSMIT: ECHOSOUNDER_RAW_DATA_RECORD_FORMAT,
DataRecordType.STRING: STRING_DATA_RECORD_FORMAT,
}
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,775 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/combine.py | import itertools
import re
from collections import ChainMap
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
from warnings import warn
import fsspec
import numpy as np
import pandas as pd
import xarray as xr
from datatree import DataTree
from ..utils.io import validate_output_path
from ..utils.log import _init_logger
from ..utils.prov import echopype_prov_attrs
from .echodata import EchoData
logger = _init_logger(__name__)
POSSIBLE_TIME_DIMS = {"time1", "time2", "time3", "ping_time"}
APPEND_DIMS = {"filenames"}.union(POSSIBLE_TIME_DIMS)
DATE_CREATED_ATTR = "date_created"
CONVERSION_TIME_ATTR = "conversion_time"
ED_GROUP = "echodata_group"
ED_FILENAME = "echodata_filename"
FILENAMES = "filenames"
def check_zarr_path(
zarr_path: Union[str, Path], storage_options: Dict[str, Any] = {}, overwrite: bool = False
) -> str:
"""
Checks that the zarr path provided to ``combine``
is valid.
Parameters
----------
zarr_path: str or Path
The full save path to the final combined zarr store
storage_options: dict
Any additional parameters for the storage
backend (ignored for local paths)
overwrite: bool
If True, will overwrite the zarr store specified by
``zarr_path`` if it already exists, otherwise an error
will be returned if the file already exists.
Returns
-------
str
The validated zarr path
Raises
------
ValueError
If the provided zarr path does not point to a zarr file
RuntimeError
If ``zarr_path`` already exists and ``overwrite=False``
"""
if zarr_path is not None:
# ensure that zarr_path is a string or Path object, throw an error otherwise
if not isinstance(zarr_path, (str, Path)):
raise TypeError("The provided zarr_path input must be of type string or pathlib.Path!")
# check that the appropriate suffix was provided
if not (Path(zarr_path).suffix == ".zarr"):
raise ValueError("The provided zarr_path input must have a '.zarr' suffix!")
# set default source_file name (will be used only if zarr_path is None)
source_file = "combined_echodata.zarr"
validated_path = validate_output_path(
source_file=source_file,
engine="zarr",
output_storage_options=storage_options,
save_path=zarr_path,
)
# convert created validated_path to a string if it is in other formats,
# since fsspec only accepts strings
if isinstance(validated_path, Path):
validated_path = str(validated_path.absolute())
# check if validated_path already exists
fs = fsspec.get_mapper(validated_path, **storage_options).fs # get file system
exists = True if fs.exists(validated_path) else False
if exists and not overwrite:
raise RuntimeError(
f"{validated_path} already exists, please provide a "
"different path or set overwrite=True."
)
elif exists and overwrite:
logger.info(f"overwriting {validated_path}")
# remove zarr file
fs.rm(validated_path, recursive=True)
return validated_path
def _check_channel_selection_form(
channel_selection: Optional[Union[List, Dict[str, list]]] = None
) -> None:
"""
Ensures that the provided user input ``channel_selection`` is in
an acceptable form.
Parameters
----------
channel_selection: list of str or dict, optional
Specifies what channels should be selected for an ``EchoData`` group
with a ``channel`` dimension (before combination).
"""
# check that channel selection is None, a list, or a dict
if not isinstance(channel_selection, (type(None), list, dict)):
raise TypeError("The input channel_selection does not have an acceptable type!")
if isinstance(channel_selection, list):
# make sure each element is a string
are_elem_str = [isinstance(elem, str) for elem in channel_selection]
if not all(are_elem_str):
raise TypeError("Each element of channel_selection must be a string!")
if isinstance(channel_selection, dict):
# make sure all keys are strings
are_keys_str = [isinstance(elem, str) for elem in channel_selection.keys()]
if not all(are_keys_str):
raise TypeError("Each key of channel_selection must be a string!")
# make sure all keys are of the form Sonar/Beam_group using regular expression
are_keys_right_form = [
True if re.match("Sonar/Beam_group(\d{1})", elem) else False # noqa
for elem in channel_selection.keys()
]
if not all(are_keys_right_form):
raise TypeError(
"Each key of channel_selection can only be a beam group path of "
"the form Sonar/Beam_group!"
)
# make sure all values are a list
are_vals_list = [isinstance(elem, list) for elem in channel_selection.values()]
if not all(are_vals_list):
raise TypeError("Each value of channel_selection must be a list!")
# make sure all values are a list of strings
are_vals_list_str = [set(map(type, elem)) == {str} for elem in channel_selection]
if not all(are_vals_list_str):
raise TypeError("Each value of channel_selection must be a list of strings!")
def check_eds(echodata_list: List[EchoData]) -> Tuple[str, List[str]]:
"""
Ensures that the input list of ``EchoData`` objects for ``combine_echodata``
is in the correct form and all necessary items exist.
Parameters
----------
echodata_list: list of EchoData object
The list of `EchoData` objects to be combined.
Returns
-------
sonar_model : str
The sonar model used for all values in ``echodata_list``
echodata_filenames : list of str
The source files names for all values in ``echodata_list``
Raises
------
TypeError
If a list of ``EchoData`` objects are not provided
ValueError
If any ``EchoData`` object's ``sonar_model`` is ``None``
ValueError
If and ``EchoData`` object does not have a file path
ValueError
If the provided ``EchoData`` objects have the same filenames
"""
# make sure that the input is a list of EchoData objects
if not isinstance(echodata_list, list) and all(
[isinstance(ed, EchoData) for ed in echodata_list]
):
raise TypeError("The input, eds, must be a list of EchoData objects!")
# get the sonar model for the combined object
if echodata_list[0].sonar_model is None:
raise ValueError("all EchoData objects must have non-None sonar_model values")
else:
sonar_model = echodata_list[0].sonar_model
echodata_filenames = []
for ed in echodata_list:
# check sonar model
if ed.sonar_model is None:
raise ValueError("all EchoData objects must have non-None sonar_model values")
elif ed.sonar_model != sonar_model:
raise ValueError("all EchoData objects must have the same sonar_model value")
# check for file names and store them
if ed.source_file is not None:
filepath = ed.source_file
elif ed.converted_raw_path is not None:
filepath = ed.converted_raw_path
else:
# defaulting to none, must be from memory
filepath = None
# set default filename to internal memory
filename = "internal-memory"
if filepath is not None:
filename = Path(filepath).name
if filename in echodata_filenames:
raise ValueError("EchoData objects have conflicting filenames")
echodata_filenames.append(filename)
return sonar_model, echodata_filenames
def _check_channel_consistency(
all_chan_list: List, ed_group: str, channel_selection: Optional[List[str]] = None
) -> None:
"""
If ``channel_selection = None``, checks that each element in ``all_chan_list`` are
the same, else makes sure that each element in ``all_chan_list`` contains all channel
names in ``channel_selection``.
Parameters
----------
all_chan_list: list of list
A list whose elements correspond to the Datasets to be combined with
their values set as a list of the channel dimension names in the Dataset
ed_group: str
The EchoData group path that produced ``all_chan_list``
channel_selection: list of str, optional
A list of channel names, which should be a subset of each
element in ``all_chan_list``
Raises
------
RuntimeError
If ``channel_selection=None`` and all ``channel`` dimensions are not the
same across all Datasets.
NotImplementedError
If ``channel_selection`` is a list and the listed channels are not contained
in the ``EchoData`` group for all Datasets and need to be created and
padded with NaN. This "expansion" type of combination has not been implemented.
"""
if channel_selection is None:
# sort each element in list, so correct comparison can be made
all_chan_list = list(map(sorted, all_chan_list))
# determine if the channels are the same across all Datasets
all_chans_equal = [all_chan_list[0]] * len(all_chan_list) == all_chan_list
if not all_chans_equal:
# obtain all unique channel names
unique_channels = set(itertools.chain.from_iterable(all_chan_list))
# raise an error if we have varying channel lengths
raise RuntimeError(
f"For the EchoData group {ed_group} the channels: {unique_channels} are "
f"not found in all EchoData objects being combined. Select which "
f"channels should be included in the combination using the keyword argument "
f"channel_selection in combine_echodata."
)
else:
# make channel_selection a set, so it is easier to use
channel_selection = set(channel_selection)
# TODO: if we will allow for expansion, then the below code should be
# replaced with a code section that makes sure the selected channels
# appear at least once in one of the other Datasets
# determine if channel selection is in each element of all_chan_list
eds_num_chan = [
channel_selection.intersection(set(ed_chans)) == channel_selection
for ed_chans in all_chan_list
]
if not all(eds_num_chan):
# raise a not implemented error if expansion (i.e. padding is necessary)
raise NotImplementedError(
f"For the EchoData group {ed_group}, some EchoData objects do "
f"not contain the selected channels. This type of combine is "
f"not currently implemented."
)
def _create_channel_selection_dict(
sonar_model: str,
has_chan_dim: Dict[str, bool],
user_channel_selection: Optional[Union[List, Dict[str, list]]] = None,
) -> Dict[str, Optional[list]]:
"""
Constructs the dictionary ``channel_selection_dict``, which specifies
the ``channel`` dimension names that should be selected for each
``EchoData`` group. If a group does not have a ``channel`` dimension
the dictionary value will be set to ``None``
Parameters
----------
sonar_model: str
The name of the sonar model corresponding to ``has_chan_dim``
has_chan_dim: dict
A dictionary created using an ``EchoData`` object whose keys are
the ``EchoData`` groups and whose values specify if that
particular group has a ``channel`` dimension
user_channel_selection: list or dict, optional
A user provided input that will be used to construct the values of
``channel_selection_dict`` (see below for further details)
Returns
-------
channel_selection_dict : dict
A dictionary with the same keys as ``has_chan_dim`` and values
determined by ``sonar_model`` and ``user_channel_selection`` as follows:
- If ``user_channel_selection=None``, then the values of the dictionary
will be set to ``None``
- If ``user_channel_selection`` is a list, then all keys corresponding to
an ``EchoData`` group with a ``channel`` dimension will have their values
set to the provided list and all other groups will be set to ``None``
- If ``user_channel_selection`` is a dictionary, then all keys corresponding to
an ``EchoData`` group without a ``channel`` dimension will have their values
set as ``None`` and the other group's values will be set as follows:
- If ``sonar_model`` is not EK80-like then all values will be set to
the union of the values of ``user_channel_selection``
- If ``sonar_model`` is EK80-like then the groups ``Sonar, Platform, Vendor_specific``
will be set to the union of the values of ``user_channel_selection`` and the rest of
the groups will be set to the same value in ``user_channel_selection`` with the same key
Notes
-----
See ``tests/echodata/test_echodata_combine.py::test_create_channel_selection_dict`` for example
outputs from this function.
"""
# base case where the user did not provide selected channels (will be used downstream)
if user_channel_selection is None:
return {grp: None for grp in has_chan_dim.keys()}
# obtain the union of all channels for each beam group
if isinstance(user_channel_selection, list):
union_beam_chans = user_channel_selection[:]
else:
union_beam_chans = list(set(itertools.chain.from_iterable(user_channel_selection.values())))
# make channel_selection dictionary where the keys are the EchoData groups and the
# values are based on the user provided input user_channel_selection
channel_selection_dict = dict()
for ed_group, has_chan in has_chan_dim.items():
# if there are no channel dimensions in the group, set the value to None
if has_chan:
if (
(not isinstance(user_channel_selection, list))
and (sonar_model in ["EK80", "ES80", "EA640"])
and (ed_group not in ["Sonar", "Platform", "Vendor_specific"])
):
# set value to the user provided input with the same key
channel_selection_dict[ed_group] = user_channel_selection[ed_group]
else:
# set value to the union of the values of user_channel_selection
channel_selection_dict[ed_group] = union_beam_chans
# sort channel names to produce consistent output (since we may be using sets)
channel_selection_dict[ed_group].sort()
else:
channel_selection_dict[ed_group] = None
return channel_selection_dict
def _check_echodata_channels(
echodata_list: List[EchoData],
user_channel_selection: Optional[Union[List, Dict[str, list]]] = None,
) -> Dict[str, Optional[List[str]]]:
"""
Coordinates the routines that check to make sure each ``EchoData`` group with a ``channel``
dimension has consistent channels for all elements in ``echodata_list``, taking into account
the input ``user_channel_selection``.
Parameters
----------
echodata_list: list of EchoData object
The list of ``EchoData`` objects to be combined
user_channel_selection: list or dict, optional
A user provided input that will be used to specify which channels will be
selected for each ``EchoData`` group
Returns
-------
dict
A dictionary with keys corresponding to the ``EchoData`` groups and
values specifying the channels that should be selected within that group.
For more information on this dictionary see the function ``_create_channel_selection_dict``.
Raises
------
RuntimeError
If any ``EchoData`` group has a ``channel`` dimension value
with a duplicate value.
Notes
-----
For further information on what is deemed consistent, please see the
function ``_check_channel_consistency``.
"""
# determine if the EchoData group contains a channel dimension
has_chan_dim = {
grp: "channel" in echodata_list[0][grp].dims for grp in echodata_list[0].group_paths
}
# create dictionary specifying the channels that should be selected for each group
channel_selection = _create_channel_selection_dict(
echodata_list[0].sonar_model, has_chan_dim, user_channel_selection
)
for ed_group in echodata_list[0].group_paths:
if "channel" in echodata_list[0][ed_group].dims:
# get each EchoData's channels as a list of list
all_chan_list = [list(ed[ed_group].channel.values) for ed in echodata_list]
# make sure each EchoData does not have repeating channels
all_chan_unique = [len(set(ed_chans)) == len(ed_chans) for ed_chans in all_chan_list]
if not all(all_chan_unique):
# get indices of EchoData objects with repeating channel names
false_ind = [ind for ind, x in enumerate(all_chan_unique) if not x]
# get files that produced the EchoData objects with repeated channels
files_w_rep_chan = [
echodata_list[ind]["Provenance"].source_filenames.values[0] for ind in false_ind
]
raise RuntimeError(
f"The EchoData objects produced by the following files "
f"have a channel dimension with repeating values, "
f"combine cannot be used: {files_w_rep_chan}"
)
# perform a consistency check for the channel dims across all Datasets
_check_channel_consistency(all_chan_list, ed_group, channel_selection[ed_group])
return channel_selection
def _check_ascending_ds_times(ds_list: List[xr.Dataset], ed_group: str) -> None:
"""
A minimal check that the first time value of each Dataset is less than
the first time value of the subsequent Dataset. If each first time value
is NaT, then this check is skipped.
Parameters
----------
ds_list: list of xr.Dataset
List of Datasets to be combined
ed_group: str
The name of the ``EchoData`` group being combined
Returns
-------
None
Raises
------
RuntimeError
If the timeX dimension is not in ascending order
for the specified echodata group
"""
# get all time dimensions of the input Datasets
ed_time_dim = set(ds_list[0].dims).intersection(POSSIBLE_TIME_DIMS)
for time in ed_time_dim:
# gather the first time of each Dataset
first_times = []
for ds in ds_list:
times = ds[time].values
if isinstance(times, np.ndarray):
# store first time if we have an array
first_times.append(times[0])
else:
# store first time if we have a single value
first_times.append(times)
first_times = np.array(first_times)
# skip check if all first times are NaT
if not np.isnan(first_times).all():
is_descending = (np.diff(first_times) < np.timedelta64(0, "ns")).any()
if is_descending:
raise RuntimeError(
f"The coordinate {time} is not in ascending order for "
f"group {ed_group}, combine cannot be used!"
)
def _check_no_append_vendor_params(
ds_list: List[xr.Dataset], ed_group: Literal["Vendor_specific"], ds_append_dims: set
) -> None:
"""
Check for identical params for all inputs without an
appending dimension in Vendor specific group
Parameters
----------
ds_list: list of xr.Dataset
List of Datasets to be combined
ed_group: "Vendor_specific"
The name of the ``EchoData`` group being combined,
this only works for "Vendor_specific" group.
ds_append_dims: set
A set of datasets append dimensions
Returns
-------
None
Raises
------
ValueError
If ``ed_group`` is not ``Vendor_specific``.
RuntimeError
If non identical filter parameters is found.
"""
if ed_group != "Vendor_specific":
raise ValueError("Group must be `Vendor_specific`!")
if len(ds_append_dims) > 0:
# If there's a dataset appending dimension, drop for comparison
# of the other values... everything else should be identical
ds_list = [ds.drop_dims(ds_append_dims) for ds in ds_list]
it = iter(ds_list)
# Init as identical, must stay True.
is_identical = True
dataset = next(it)
for next_dataset in it:
is_identical = dataset.identical(next_dataset)
if not is_identical:
raise RuntimeError(
f"Non identical filter parameters in {ed_group} group. " "Objects cannot be merged!"
)
dataset = next_dataset
def _merge_attributes(attributes: List[Dict[str, str]]) -> Dict[str, str]:
"""
Merge a list of attributes dictionary
Parameters
----------
attributes : list of dict
List of attributes dictionary
E.g. [{'attr1': 'val1'}, {'attr2': 'val2'}, ...]
Returns
-------
dict
The merged attribute dictionary
"""
merged_dict = {}
for attribute in attributes:
for key, value in attribute.items():
if key not in merged_dict:
# if current key is not in merged attribute,
# then save the value for that key
merged_dict[key] = value
elif merged_dict[key] == "":
# if current key is already in merged attribute,
# check if the value of that key is empty,
# in this case overwrite the value with current value
merged_dict[key] = value
# By default the rest of the behavior
# will keep the first non-empty value it sees
# NOTE: @lsetiawan (6/2/2023) - Comment this out for now until
# attributes are fully evaluated by @leewujung and @emiliom
# if value == "" and key not in merged_dict:
# # checks if current attr value is empty,
# # and doesn't exist in merged attribute,
# # saving the first non empty value only
# merged_dict[key] = value
# elif value != "":
# # if current attr value is not empty,
# # then overwrite the merged attribute,
# # keeping attribute from latest value
# merged_dict[key] = value
return merged_dict
def _capture_prov_attrs(
attrs_dict: Dict[str, List[Dict[str, str]]], echodata_filenames: List[str], sonar_model: str
) -> xr.Dataset:
"""
Capture and create provenance dataset,
from the combined attribute values.
Parameters
----------
attrs_dict : dict of list
Dictionary of attributes for each of the group.
E.g. {'Group': [{'attr1': 'val1'}, {'attr2': 'val2'}, ...]}
echodata_filenames : list of str
The filenames of the echodata objects
sonar_model : str
The sonar model
Returns
-------
xr.Dataset
The provenance dataset for all attribute values from
the list of echodata objects that are combined.
"""
ds_list = []
for group, attributes in attrs_dict.items():
df = pd.DataFrame.from_records(attributes)
df.loc[:, ED_FILENAME] = echodata_filenames
df = df.set_index(ED_FILENAME)
group_ds = df.to_xarray()
for _, var in group_ds.data_vars.items():
var.attrs.update({ED_GROUP: group})
ds_list.append(group_ds)
prov_ds = xr.merge(ds_list)
# Set these provenance as string
prov_ds = prov_ds.fillna("").astype(str)
prov_ds[ED_FILENAME] = prov_ds[ED_FILENAME].astype(str)
return prov_ds
def _get_prov_attrs(
ds: xr.Dataset, is_combined: bool = True
) -> Optional[Dict[str, List[Dict[str, str]]]]:
"""
Get the provenance attributes from the dataset.
This function is meant to be used on an already combined dataset.
Parameters
----------
ds : xr.Dataset
The Provenance group dataset to get attributes from
is_combined: bool
The flag to indicate if it's combined
Returns
-------
Dict[str, List[Dict[str, str]]]
The provenance attributes
"""
if is_combined:
attrs_dict = {}
for k, v in ds.data_vars.items():
# Go through each data variable and extract the attribute values
# based on the echodata group as stored in the variable attribute
if ED_GROUP in v.attrs:
ed_group = v.attrs[ED_GROUP]
if ed_group not in attrs_dict:
attrs_dict[ed_group] = []
# Store the values as a list of dictionary for each group
attrs_dict[ed_group].append([{k: i} for i in v.values])
# Merge the attributes for each group so it matches the
# attributes dict for later merging
return {
ed_group: [
dict(ChainMap(*v))
for _, v in pd.DataFrame.from_dict(attrs).to_dict(orient="list").items()
]
for ed_group, attrs in attrs_dict.items()
}
return None
def _combine(
sonar_model: str,
eds: List[EchoData] = [],
echodata_filenames: List[str] = [],
ed_group_chan_sel: Dict[str, Optional[List[str]]] = {},
) -> Dict[str, xr.Dataset]:
"""
Combines the echodata objects and export to a dictionary tree.
Parameters
----------
sonar_model : str
The sonar model used for all elements in ``eds``
eds: list of EchoData object
The list of ``EchoData`` objects to be combined
echodata_filenames : list of str
The filenames of the echodata objects
ed_group_chan_sel: dict
A dictionary with keys corresponding to the ``EchoData`` groups
and values specify what channels should be selected within that
group. If a value is ``None``, then a subset of channels should
not be selected.
Returns
-------
dict of xr.Dataset
The dictionary tree containing the xarray dataset
for each of the combined group
"""
all_group_paths = dict.fromkeys(
itertools.chain.from_iterable([list(ed.group_paths) for ed in eds])
).keys()
# For dealing with attributes
attrs_dict = {}
# Check if input data are combined datasets
# Create combined mapping for later use
combined_mapping = []
for idx, ed in enumerate(eds):
is_combined = ed["Provenance"].attrs.get("is_combined", False)
combined_mapping.append(
{
"is_combined": is_combined,
"attrs_dict": _get_prov_attrs(ed["Provenance"], is_combined),
"echodata_filename": [str(s) for s in ed["Provenance"][ED_FILENAME].values]
if is_combined
else [echodata_filenames[idx]],
}
)
# Get single boolean value to see if there's any combined files
any_combined = any(d["is_combined"] for d in combined_mapping)
if any_combined:
# Fetches the true echodata filenames if there are any combined files
echodata_filenames = list(
itertools.chain.from_iterable([d[ED_FILENAME] for d in combined_mapping])
)
# Create Echodata tree dict
tree_dict = {}
for ed_group in all_group_paths:
# collect the group Dataset from all eds that have their channels unselected
all_chan_ds_list = [ed[ed_group] for ed in eds]
# select only the appropriate channels from each Dataset
ds_list = [
ds.sel(channel=ed_group_chan_sel[ed_group])
if ed_group_chan_sel[ed_group] is not None
else ds
for ds in all_chan_ds_list
]
if ds_list:
if not any_combined:
# Get all of the keys and attributes
# for regular non combined echodata object
ds_attrs = [ds.attrs for ds in ds_list]
else:
# If there are any combined files,
# iterate through from mapping above
ds_attrs = []
for idx, ds in enumerate(ds_list):
# Retrieve the echodata attrs dict
# parsed from provenance group above
ed_attrs_dict = combined_mapping[idx]["attrs_dict"]
if ed_attrs_dict is not None:
# Set attributes to the appropriate group
# from echodata attrs provenance,
# set default empty dict for missing group
attrs = ed_attrs_dict.get(ed_group, {})
else:
# This is for non combined echodata object
attrs = [ds.attrs]
ds_attrs += attrs
# Attribute holding
attrs_dict[ed_group] = ds_attrs
# Checks for ascending time in dataset list
_check_ascending_ds_times(ds_list, ed_group)
# get all dimensions in ds that are append dimensions
ds_append_dims = set(ds_list[0].dims).intersection(APPEND_DIMS)
# Checks for filter parameters for "Vendor_specific" ONLY
if ed_group == "Vendor_specific":
_check_no_append_vendor_params(ds_list, ed_group, ds_append_dims)
if len(ds_append_dims) == 0:
combined_ds = ds_list[0]
else:
combined_ds = xr.Dataset()
for dim in ds_append_dims:
drop_dims = [c_dim for c_dim in ds_append_dims if c_dim != dim]
sub_ds = xr.concat(
[ds.drop_dims(drop_dims) for ds in ds_list],
dim=dim,
coords="minimal",
data_vars="minimal",
compat="no_conflicts",
)
combined_ds = combined_ds.assign(sub_ds.variables)
# Modify default attrs
if ed_group == "Top-level":
ed_group = "/"
# Merge attributes and set to dataset
group_attrs = _merge_attributes(ds_attrs)
# Empty out attributes for now, will be refilled later
combined_ds.attrs = group_attrs
# Add combined flag and update conversion time for Provenance
if ed_group == "Provenance":
combined_ds.attrs.update(
{
"is_combined": True,
"conversion_software_name": group_attrs["conversion_software_name"],
"conversion_software_version": group_attrs["conversion_software_version"],
"conversion_time": group_attrs["conversion_time"],
}
)
prov_dict = echopype_prov_attrs(process_type="combination")
combined_ds = combined_ds.assign_attrs(prov_dict)
# Data holding
tree_dict[ed_group] = combined_ds
# Capture provenance for all the attributes
prov_ds = _capture_prov_attrs(attrs_dict, echodata_filenames, sonar_model)
if not any_combined:
# Update the provenance dataset with the captured data
prov_ds = tree_dict["Provenance"].assign(prov_ds)
else:
prov_ds = tree_dict["Provenance"].drop_dims(ED_FILENAME).assign(prov_ds)
# Update filenames to iter integers
prov_ds[FILENAMES] = prov_ds[FILENAMES].copy(data=np.arange(*prov_ds[FILENAMES].shape)) # noqa
tree_dict["Provenance"] = prov_ds
return tree_dict
def combine_echodata(
echodata_list: List[EchoData] = None,
channel_selection: Optional[Union[List, Dict[str, list]]] = None,
) -> EchoData:
"""
Combines multiple ``EchoData`` objects into a single ``EchoData`` object.
Parameters
----------
echodata_list : list of EchoData object
The list of ``EchoData`` objects to be combined
channel_selection: list of str or dict, optional
Specifies what channels should be selected for an ``EchoData`` group
with a ``channel`` dimension (before combination).
- if a list is provided, then each ``EchoData`` group with a ``channel`` dimension
will only contain the channels in the provided list
- if a dictionary is provided, the dictionary should have keys specifying only beam
groups (e.g. "Sonar/Beam_group1") and values as a list of channel names to select
within that beam group. The rest of the ``EchoData`` groups with a ``channel`` dimension
will have their selected channels chosen automatically.
Returns
-------
EchoData
A lazy loaded ``EchoData`` object,
with all data from the input ``EchoData`` objects combined.
Raises
------
ValueError
If the provided zarr path does not point to a zarr file
TypeError
If a list of ``EchoData`` objects are not provided
ValueError
If any ``EchoData`` object's ``sonar_model`` is ``None``
ValueError
If any ``EchoData`` object does not have a file path
ValueError
If the provided ``EchoData`` objects have the same filenames
RuntimeError
If the first time value of each ``EchoData`` group is not less
than the first time value of the subsequent corresponding
``EchoData`` group, with respect to the order in ``echodata_list``
RuntimeError
If the same ``EchoData`` groups in ``echodata_list`` do not
have the same number of channels and the same name for each
of these channels.
RuntimeError
If any of the following attribute checks are not met
amongst the combined ``EchoData`` groups:
- the keys are not the same
- the values are not identical
- the keys ``date_created`` or ``conversion_time``
do not have the same types
RuntimeError
If any ``EchoData`` group has a ``channel`` dimension value
with a duplicate value.
RuntimeError
If ``channel_selection=None`` and the ``channel`` dimensions are not the
same across the same group under each object in ``echodata_list``.
NotImplementedError
If ``channel_selection`` is a list and the listed channels are not contained
in the ``EchoData`` group across all objects in ``echodata_list``.
Notes
-----
* ``EchoData`` objects are combined by appending their groups individually.
* All attributes (besides attributes whose values are arrays) from all groups before the
combination will be stored in the ``Provenance`` group.
Examples
--------
Combine lazy loaded ``EchoData`` objects:
>>> ed1 = echopype.open_converted("file1.zarr")
>>> ed2 = echopype.open_converted("file2.zarr")
>>> combined = echopype.combine_echodata(echodata_list=[ed1, ed2])
Combine in-memory ``EchoData`` objects:
>>> ed1 = echopype.open_raw(raw_file="EK60_file1.raw", sonar_model="EK60")
>>> ed2 = echopype.open_raw(raw_file="EK60_file2.raw", sonar_model="EK60")
>>> combined = echopype.combine_echodata(echodata_list=[ed1, ed2])
"""
# return empty EchoData object, if no EchoData objects are provided
if echodata_list is None:
warn("No EchoData objects were provided, returning an empty EchoData object.")
return EchoData()
# Ensure the list of all EchoData objects to be combined are valid
sonar_model, echodata_filenames = check_eds(echodata_list)
# make sure channel_selection is the appropriate type and only contains the beam groups
_check_channel_selection_form(channel_selection)
# perform channel check and get channel selection for each EchoData group
ed_group_chan_sel = _check_echodata_channels(echodata_list, channel_selection)
# combine the echodata objects and get the tree dict
tree_dict = _combine(
sonar_model=sonar_model,
eds=echodata_list,
echodata_filenames=echodata_filenames,
ed_group_chan_sel=ed_group_chan_sel,
)
# create datatree from tree dictionary
tree = DataTree.from_dict(tree_dict, name="root")
# create echodata object from datatree
ed_comb = EchoData(sonar_model=sonar_model)
ed_comb._set_tree(tree)
ed_comb._load_tree()
return ed_comb
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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,776 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/consolidate/test_consolidate_integration.py | import math
import os
import pathlib
import tempfile
import pytest
import numpy as np
import pandas as pd
import xarray as xr
import scipy.io as io
import echopype as ep
from typing import List
"""
For future reference:
For ``test_add_splitbeam_angle`` the test data is in the following locations:
- the EK60 raw file is in `test_data/ek60/DY1801_EK60-D20180211-T164025.raw` and the
associated echoview split-beam data is in `test_data/ek60/splitbeam`.
- the EK80 raw file is in `test_data/ek80_bb_with_calibration/2018115-D20181213-T094600.raw` and
the associated echoview split-beam data is in `test_data/ek80_bb_with_calibration/splitbeam`
"""
@pytest.fixture(
params=[
(
("EK60", "DY1002_EK60-D20100318-T023008_rep_freq.raw"),
"EK60",
None,
{},
),
(
("EK80_NEW", "D20211004-T233354.raw"),
"EK80",
None,
{'waveform_mode': 'CW', 'encode_mode': 'power'},
),
(
("AZFP", "17082117.01A"),
"AZFP",
("AZFP", "17041823.XML"),
{},
),
],
ids=[
"ek60_dup_freq",
"ek80_cw_power",
"azfp",
],
)
def test_data_samples(request, test_path):
(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
) = request.param
path_model, *paths = filepath
filepath = test_path[path_model].joinpath(*paths)
if azfp_xml_path is not None:
path_model, *paths = azfp_xml_path
azfp_xml_path = test_path[path_model].joinpath(*paths)
return (
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
)
def _check_swap(ds, ds_swap):
assert "channel" in ds.dims
assert "frequency_nominal" not in ds.dims
assert "frequency_nominal" in ds_swap.dims
assert "channel" not in ds_swap.dims
def test_swap_dims_channel_frequency(test_data_samples):
"""
Test swapping dimension/coordinate from channel to frequency_nominal.
"""
(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
) = test_data_samples
ed = ep.open_raw(filepath, sonar_model, azfp_xml_path)
if ed.sonar_model.lower() == 'azfp':
avg_temperature = ed['Environment']['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
range_kwargs['env_params'] = env_params
if 'azfp_cal_type' in range_kwargs:
range_kwargs.pop('azfp_cal_type')
dup_freq_valueerror = (
"Duplicated transducer nominal frequencies exist in the file. "
"Operation is not valid."
)
Sv = ep.calibrate.compute_Sv(ed, **range_kwargs)
try:
Sv_swapped = ep.consolidate.swap_dims_channel_frequency(Sv)
_check_swap(Sv, Sv_swapped)
except Exception as e:
assert isinstance(e, ValueError) is True
assert str(e) == dup_freq_valueerror
MVBS = ep.commongrid.compute_MVBS(Sv)
try:
MVBS_swapped = ep.consolidate.swap_dims_channel_frequency(MVBS)
_check_swap(Sv, MVBS_swapped)
except Exception as e:
assert isinstance(e, ValueError) is True
assert str(e) == dup_freq_valueerror
def _build_ds_Sv(channel, range_sample, ping_time, sample_interval):
return xr.Dataset(
data_vars={
"Sv": (
("channel", "range_sample", "ping_time"),
np.random.random((len(channel), range_sample.size, ping_time.size)),
),
"echo_range": (
("channel", "range_sample", "ping_time"),
(
np.swapaxes(np.tile(range_sample, (len(channel), ping_time.size, 1)), 1, 2)
* sample_interval
),
),
},
coords={
"channel": channel,
"range_sample": range_sample,
"ping_time": ping_time,
},
)
def test_add_depth():
# Build test Sv dataset
channel = ["channel_0", "channel_1", "channel_2"]
range_sample = np.arange(100)
ping_time = pd.date_range(start="2022-08-10T10:00:00", end="2022-08-10T12:00:00", periods=121)
sample_interval = 0.01
ds_Sv = _build_ds_Sv(channel, range_sample, ping_time, sample_interval)
# # no water_level in ds
# try:
# ds_Sv_depth = ep.consolidate.add_depth(ds_Sv)
# except ValueError:
# ...
# user input water_level
water_level = 10
ds_Sv_depth = ep.consolidate.add_depth(ds_Sv, depth_offset=water_level)
assert ds_Sv_depth["depth"].equals(ds_Sv["echo_range"] + water_level)
# user input water_level and tilt
tilt = 15
ds_Sv_depth = ep.consolidate.add_depth(ds_Sv, depth_offset=water_level, tilt=tilt)
assert ds_Sv_depth["depth"].equals(ds_Sv["echo_range"] * np.cos(tilt / 180 * np.pi) + water_level)
# inverted echosounder
ds_Sv_depth = ep.consolidate.add_depth(ds_Sv, depth_offset=water_level, tilt=tilt, downward=False)
assert ds_Sv_depth["depth"].equals(-1 * ds_Sv["echo_range"] * np.cos(tilt / 180 * np.pi) + water_level)
# check attributes
# assert ds_Sv_depth["depth"].attrs == {"long_name": "Depth", "standard_name": "depth"}
def _create_array_list_from_echoview_mats(paths_to_echoview_mat: List[pathlib.Path]) -> List[np.ndarray]:
"""
Opens each mat file in ``paths_to_echoview_mat``, selects the first ``ping_time``,
and then stores the array in a list.
Parameters
----------
paths_to_echoview_mat: list of pathlib.Path
A list of paths corresponding to mat files, where each mat file contains the
echoview generated angle alongship and athwartship data for a channel
Returns
-------
list of np.ndarray
A list of numpy arrays generated by choosing the appropriate data from the mat files.
This list will have the same length as ``paths_to_echoview_mat``
"""
list_of_mat_arrays = []
for mat_file in paths_to_echoview_mat:
# open mat file and grab appropriate data
list_of_mat_arrays.append(io.loadmat(file_name=mat_file)["P0"]["Data_values"][0][0])
return list_of_mat_arrays
@pytest.mark.parametrize(
["location_type", "sonar_model", "path_model", "raw_and_xml_paths", "extras"],
[
(
"empty-location",
"EK60",
"EK60",
("ooi/CE02SHBP-MJ01C-07-ZPLSCB101_OOI-D20191201-T000000.raw", None),
None,
),
(
"with-track-location",
"EK60",
"EK60",
("Winter2017-D20170115-T150122.raw", None),
None,
),
(
"fixed-location",
"AZFP",
"AZFP",
("17082117.01A", "17041823.XML"),
{'longitude': -60.0, 'latitude': 45.0, 'salinity': 27.9, 'pressure': 59},
),
],
)
def test_add_location(
location_type,
sonar_model,
path_model,
raw_and_xml_paths,
extras,
test_path
):
# Prepare the Sv dataset
raw_path = test_path[path_model] / raw_and_xml_paths[0]
if raw_and_xml_paths[1]:
xml_path = test_path[path_model] / raw_and_xml_paths[1]
else:
xml_path = None
ed = ep.open_raw(raw_path, xml_path=xml_path, sonar_model=sonar_model)
if location_type == "fixed-location":
point_ds = xr.Dataset(
{
"latitude": (["time"], np.array([float(extras['latitude'])])),
"longitude": (["time"], np.array([float(extras['longitude'])])),
},
coords={
"time": (["time"], np.array([ed["Sonar/Beam_group1"]["ping_time"].values.min()]))
},
)
ed.update_platform(point_ds, variable_mappings={"latitude": "latitude", "longitude": "longitude"})
env_params = None
# AZFP data require external salinity and pressure
if sonar_model == "AZFP":
env_params = {
"temperature": ed["Environment"]["temperature"].values.mean(),
"salinity": extras["salinity"],
"pressure": extras["pressure"],
}
ds = ep.calibrate.compute_Sv(echodata=ed, env_params=env_params)
# add_location tests
if location_type == "empty-location":
with pytest.raises(Exception) as exc:
ep.consolidate.add_location(ds=ds, echodata=ed)
assert exc.type is ValueError
assert "Coordinate variables not present or all nan" in str(exc.value)
else:
def _tests(ds_test, location_type, nmea_sentence=None):
# lat,lon & time1 existence
assert "latitude" in ds_test
assert "longitude" in ds_test
assert "time1" not in ds_test
# lat & lon have a single dimension: 'ping_time'
assert len(ds_test["longitude"].dims) == 1 and ds_test["longitude"].dims[0] == "ping_time" # noqa
assert len(ds_test["latitude"].dims) == 1 and ds_test["latitude"].dims[0] == "ping_time" # noqa
# Check interpolated or broadcast values
if location_type == "with-track-location":
for position in ["longitude", "latitude"]:
position_var = ed["Platform"][position]
if nmea_sentence:
position_var = position_var[ed["Platform"]["sentence_type"] == nmea_sentence]
position_interp = position_var.interp(time1=ds_test["ping_time"])
# interpolated values are identical
assert np.allclose(ds_test[position].values, position_interp.values, equal_nan=True) # noqa
elif location_type == "fixed-location":
for position in ["longitude", "latitude"]:
position_uniq = set(ds_test[position].values)
# contains a single repeated value equal to the value passed to update_platform
assert (
len(position_uniq) == 1 and
math.isclose(list(position_uniq)[0], extras[position])
)
ds_all = ep.consolidate.add_location(ds=ds, echodata=ed)
_tests(ds_all, location_type)
# the test for nmea_sentence="GGA" is limited to the with-track-location case
if location_type == "with-track-location":
ds_sel = ep.consolidate.add_location(ds=ds, echodata=ed, nmea_sentence="GGA")
_tests(ds_sel, location_type, nmea_sentence="GGA")
@pytest.mark.parametrize(
("sonar_model", "test_path_key", "raw_file_name", "paths_to_echoview_mat",
"waveform_mode", "encode_mode", "pulse_compression", "write_Sv_to_file"),
[
# ek60_CW_power
(
"EK60", "EK60", "DY1801_EK60-D20180211-T164025.raw",
[
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T1.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T2.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T3.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T4.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T5.mat'
],
"CW", "power", False, False
),
# ek60_CW_power_Sv_path
(
"EK60", "EK60", "DY1801_EK60-D20180211-T164025.raw",
[
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T1.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T2.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T3.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T4.mat',
'splitbeam/DY1801_EK60-D20180211-T164025_angles_T5.mat'
],
"CW", "power", False, True
),
# ek80_CW_complex
(
"EK80", "EK80_CAL", "2018115-D20181213-T094600.raw",
[
'splitbeam/2018115-D20181213-T094600_angles_T1.mat',
'splitbeam/2018115-D20181213-T094600_angles_T4.mat',
'splitbeam/2018115-D20181213-T094600_angles_T6.mat',
'splitbeam/2018115-D20181213-T094600_angles_T5.mat'
],
"CW", "complex", False, False
),
# ek80_BB_complex_no_pc
(
"EK80", "EK80_CAL", "2018115-D20181213-T094600.raw",
[
'splitbeam/2018115-D20181213-T094600_angles_T3_nopc.mat',
'splitbeam/2018115-D20181213-T094600_angles_T2_nopc.mat',
],
"BB", "complex", False, False,
),
# ek80_CW_power
(
"EK80", "EK80", "Summer2018--D20180905-T033113.raw",
[
'splitbeam/Summer2018--D20180905-T033113_angles_T2.mat',
'splitbeam/Summer2018--D20180905-T033113_angles_T1.mat',
],
"CW", "power", False, False,
),
],
ids=[
"ek60_CW_power",
"ek60_CW_power_Sv_path",
"ek80_CW_complex",
"ek80_BB_complex_no_pc",
"ek80_CW_power",
],
)
def test_add_splitbeam_angle(sonar_model, test_path_key, raw_file_name, test_path,
paths_to_echoview_mat, waveform_mode, encode_mode,
pulse_compression, write_Sv_to_file):
# obtain the EchoData object with the data needed for the calculation
ed = ep.open_raw(test_path[test_path_key] / raw_file_name, sonar_model=sonar_model)
# compute Sv as it is required for the split-beam angle calculation
ds_Sv = ep.calibrate.compute_Sv(ed, waveform_mode=waveform_mode, encode_mode=encode_mode)
# initialize temporary directory object
temp_dir = None
# allows us to test for the case when source_Sv is a path
if write_Sv_to_file:
# create temporary directory for mask_file
temp_dir = tempfile.TemporaryDirectory()
# write DataArray to temporary directory
zarr_path = os.path.join(temp_dir.name, "Sv_data.zarr")
ds_Sv.to_zarr(zarr_path)
# assign input to a path
ds_Sv = zarr_path
# add the split-beam angles to Sv dataset
ds_Sv = ep.consolidate.add_splitbeam_angle(source_Sv=ds_Sv, echodata=ed,
waveform_mode=waveform_mode,
encode_mode=encode_mode,
pulse_compression=pulse_compression)
# obtain corresponding echoview output
full_echoview_path = [test_path[test_path_key] / path for path in paths_to_echoview_mat]
echoview_arr_list = _create_array_list_from_echoview_mats(full_echoview_path)
# compare echoview output against computed output for all channels
for chan_ind in range(len(echoview_arr_list)):
# grabs the appropriate ds data to compare against
reduced_angle_alongship = ds_Sv.isel(channel=chan_ind, ping_time=0).angle_alongship.dropna("range_sample")
reduced_angle_athwartship = ds_Sv.isel(channel=chan_ind, ping_time=0).angle_athwartship.dropna("range_sample")
# TODO: make "start" below a parameter in the input so that this is not ad-hoc but something known
# for some files the echoview data is shifted by one index, here we account for that
if reduced_angle_alongship.shape == (echoview_arr_list[chan_ind].shape[1], ):
start = 0
else:
start = 1
# note for the checks below:
# - angles from CW power data are similar down to 1e-7
# - angles computed from complex samples deviates a lot more
# check the computed angle_alongship values against the echoview output
assert np.allclose(reduced_angle_alongship.values[start:],
echoview_arr_list[chan_ind][0, :], rtol=1e-1, atol=1e-2)
# check the computed angle_alongship values against the echoview output
assert np.allclose(reduced_angle_athwartship.values[start:],
echoview_arr_list[chan_ind][1, :], rtol=1e-1, atol=1e-2)
if temp_dir:
# remove the temporary directory, if it was created
temp_dir.cleanup()
def test_add_splitbeam_angle_BB_pc(test_path):
# obtain the EchoData object with the data needed for the calculation
ed = ep.open_raw(test_path["EK80_CAL"] / "2018115-D20181213-T094600.raw", sonar_model="EK80")
# compute Sv as it is required for the split-beam angle calculation
ds_Sv = ep.calibrate.compute_Sv(ed, waveform_mode="BB", encode_mode="complex")
# add the split-beam angles to Sv dataset
ds_Sv = ep.consolidate.add_splitbeam_angle(
source_Sv=ds_Sv, echodata=ed,
waveform_mode="BB", encode_mode="complex", pulse_compression=True
)
# Load pyecholab pickle
import pickle
with open(test_path["EK80_EXT"] / "pyecholab/pyel_BB_splitbeam.pickle", 'rb') as handle:
pyel_BB_p_data = pickle.load(handle)
# Compare 70kHz channel
chan_sel = "WBT 714590-15 ES70-7C"
# Compare cal params
# dict mappgin: {pyecholab : echopype}
cal_params_dict = {
"angle_sensitivity_alongship": "angle_sensitivity_alongship",
"angle_sensitivity_athwartship": "angle_sensitivity_athwartship",
"beam_width_alongship": "beamwidth_alongship",
"beam_width_athwartship": "beamwidth_athwartship",
}
for p_pyel, p_ep in cal_params_dict.items():
assert np.allclose(pyel_BB_p_data["cal_parms"][p_pyel],
ds_Sv[p_ep].sel(channel=chan_sel).values)
# alongship angle
pyel_vals = pyel_BB_p_data["alongship_physical"]
ep_vals = ds_Sv["angle_alongship"].sel(channel=chan_sel).values
assert pyel_vals.shape == ep_vals.shape
assert np.allclose(pyel_vals, ep_vals, atol=1e-5)
# athwartship angle
pyel_vals = pyel_BB_p_data["athwartship_physical"]
ep_vals = ds_Sv["angle_athwartship"].sel(channel=chan_sel).values
assert pyel_vals.shape == ep_vals.shape
assert np.allclose(pyel_vals, ep_vals, atol=1e-6)
# TODO: need a test for power/angle data, with mock EchoData object
# containing some channels with single-beam data and some channels with split-beam data
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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,777 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/api.py | from typing import TYPE_CHECKING, Dict
if TYPE_CHECKING:
from ..core import PathHint
from .echodata import EchoData
def open_converted(
converted_raw_path: "PathHint",
storage_options: Dict[str, str] = None,
**kwargs
# kwargs: Dict[str, Any] = {'chunks': 'auto'} # TODO: do we need this?
):
"""Create an EchoData object from a single converted netcdf or zarr file.
Parameters
----------
converted_raw_path : str
path to converted data file
storage_options : dict
options for cloud storage
kwargs : dict
optional keyword arguments to be passed
into xr.open_dataset
Returns
-------
EchoData object
"""
# TODO: combine multiple files when opening
return EchoData.from_file(
converted_raw_path=converted_raw_path,
storage_options=storage_options,
open_kwargs=kwargs,
)
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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,778 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/utils/test_utils_io.py | import os
import fsspec
from pathlib import Path
import pytest
from typing import Tuple
import tempfile
import platform
import xarray as xr
from echopype.utils.io import (
sanitize_file_path,
validate_output_path,
env_indep_joinpath,
validate_source_ds_da,
init_ep_dir
)
import echopype.utils.io
@pytest.mark.parametrize(
"file_path, should_fail, file_type",
[
('https://example.com/test.nc', True, 'nc'),
('https://example.com/test.zarr', False, 'zarr'),
(os.path.join('folder', 'test.nc'), False, 'nc'),
(os.path.join('folder', 'test.zarr'), False, 'zarr'),
(Path('https:/example.com/test.nc'), True, 'nc'),
(Path('https:/example.com/test.zarr'), True, 'zarr'),
(Path('folder/test.nc'), False, 'nc'),
(Path('folder/test.zarr'), False, 'zarr'),
(fsspec.get_mapper('https://example.com/test.nc'), True, 'nc'),
(fsspec.get_mapper('https:/example.com/test.zarr'), False, 'zarr'),
(fsspec.get_mapper('folder/test.nc'), False, 'nc'),
(fsspec.get_mapper('folder/test.zarr'), False, 'zarr'),
('https://example.com/test.jpeg', True, 'jpeg'),
(Path('https://example.com/test.jpeg'), True, 'jpeg'),
(fsspec.get_mapper('https://example.com/test.jpeg'), True, 'jpeg'),
],
)
def test_sanitize_file_path(file_path, should_fail, file_type):
try:
sanitized = sanitize_file_path(file_path)
if not should_fail:
if file_type == 'nc':
assert isinstance(sanitized, Path) is True
elif file_type == 'zarr':
assert isinstance(sanitized, fsspec.FSMap) is True
except Exception as e:
assert isinstance(e, ValueError) is True
@pytest.mark.parametrize(
"save_path, engine",
[
# Netcdf tests
(os.path.join('folder', 'new_test.nc'), 'netcdf4'),
(os.path.join('folder', 'new_test.nc'), 'zarr'),
(os.path.join('folder', 'path', 'new_test.nc'), 'netcdf4'),
('folder/', 'netcdf4'),
('s3://ooi-raw-data/', 'netcdf4'),
(Path('folder/'), 'netcdf4'),
(Path('folder/new_test.nc'), 'netcdf4'),
# Zarr tests
(os.path.join('folder', 'new_test.zarr'), 'zarr'),
(os.path.join('folder', 'new_test.zarr'), 'netcdf4'),
(os.path.join('folder', 'path', 'new_test.zarr'), 'zarr'),
('folder/', 'zarr'),
# Empty tests
(None, 'netcdf4'),
(None, 'zarr'),
# Remotes
('https://example.com/test.zarr', 'zarr'),
('https://example.com/', 'zarr'),
('https://example.com/test.nc', 'netcdf4'),
('s3://ooi-raw-data/new_test.zarr', 'zarr'),
('s3://ooi-raw-data/new_test.nc', 'netcdf4'),
],
)
def test_validate_output_path(save_path, engine, minio_bucket):
output_root_path = os.path.join('.', 'echopype', 'test_data', 'dump')
source_file = 'test.raw'
if engine == 'netcdf4':
ext = '.nc'
else:
ext = '.zarr'
if save_path is not None:
if '://' not in str(save_path):
save_path = os.path.join(output_root_path, save_path)
is_dir = True if Path(save_path).suffix == '' else False
else:
is_dir = True
save_path = output_root_path
output_storage_options = {}
if save_path and save_path.startswith("s3://"):
output_storage_options = dict(
client_kwargs=dict(endpoint_url="http://localhost:9000/"),
key="minioadmin",
secret="minioadmin",
)
try:
output_path = validate_output_path(
source_file, engine, output_storage_options, save_path
)
assert isinstance(output_path, str) is True
assert Path(output_path).suffix == ext
if is_dir:
assert Path(output_path).name == source_file.replace('.raw', '') + ext
else:
output_file = Path(save_path)
assert Path(output_path).name == output_file.name.replace(output_file.suffix, '') + ext
except Exception as e:
if 'https://' in save_path:
if save_path == 'https://example.com/':
assert isinstance(e, ValueError) is True
assert str(e) == 'Input file type not supported!'
elif save_path == 'https://example.com/test.nc':
assert isinstance(e, ValueError) is True
assert str(e) == 'Only local netcdf4 is supported.'
else:
assert isinstance(e, PermissionError) is True
elif save_path == 's3://ooi-raw-data/new_test.nc':
assert isinstance(e, ValueError) is True
assert str(e) == 'Only local netcdf4 is supported.'
def mock_windows_return(*args: Tuple[str, ...]):
"""
A function to mock what ``os.path.join`` should
return on a Windows machine.
Parameters
----------
args: tuple of str
A variable number of strings to join
Returns
-------
str
The input strings joined using Windows syntax
"""
return "\\".join(args)
def mock_unix_return(*args: Tuple[str, ...]):
"""
A function to mock what ``os.path.join`` should
return on a Unix based machine.
Parameters
----------
args: tuple of str
A variable number of strings to join
Returns
-------
str
The input strings joined using Unix syntax
Notes
-----
This function is necessary just in case the tests are being
run on a Windows machine.
"""
return r"/".join(args)
@pytest.mark.parametrize(
"save_path, is_windows, is_cloud",
[
(r"/folder", False, False),
(r"C:\folder", True, False),
(r"s3://folder", False, True),
(r"s3://folder", True, True),
]
)
def test_env_indep_joinpath_mock_return(save_path: str, is_windows: bool, is_cloud: bool, monkeypatch):
"""
Tests the function ``env_indep_joinpath`` using a mock return on varying OS and cloud
path scenarios by adding a folder and a file to the input ``save_path``.
Parameters
----------
save_path: str
The save path that we want to add a folder and a file to.
is_windows: bool
If True, signifies that we are "working" on a Windows machine,
otherwise on a Unix based machine
is_cloud: bool
If True, signifies that ``save_path`` corresponds to a cloud path,
otherwise it does not
Notes
-----
This test uses a monkeypatch for ``os.path.join`` to mimic the join we expect
from the function. This allows us to test ``env_indep_joinpath`` on any OS.
"""
# assign the appropriate mock return for os.path.join
if is_windows:
monkeypatch.setattr(os.path, 'join', mock_windows_return)
else:
monkeypatch.setattr(os.path, 'join', mock_unix_return)
# add folder and file to path
joined_path = env_indep_joinpath(save_path, "output", "data.zarr")
if is_cloud or (not is_windows):
assert joined_path == (save_path + r"/output/data.zarr")
else:
assert joined_path == (save_path + r"\output\data.zarr")
@pytest.mark.parametrize(
"save_path, is_windows, is_cloud",
[
(r"/root/folder", False, False),
(r"C:\root\folder", True, False),
(r"s3://root/folder", False, True),
(r"s3://root/folder", True, True),
]
)
def test_env_indep_joinpath_os_dependent(save_path: str, is_windows: bool, is_cloud: bool):
"""
Tests the true output of the function ``env_indep_joinpath`` on varying OS and cloud path
scenarios by adding a folder and a file to the input ``save_path``.
Parameters
----------
save_path: str
The save path that we want to add a folder and a file to.
is_windows: bool
If True, signifies that we are working on a Windows machine,
otherwise on a Unix based machine
is_cloud: bool
If True, signifies that ``save_path`` corresponds to a cloud path,
otherwise it does not
Notes
-----
This test is OS dependent and the testing of parameters will be skipped if
they do not correspond to the OS they are being run on.
"""
# add folder and file to path
joined_path = env_indep_joinpath(save_path, "output", "data.zarr")
if is_cloud:
assert joined_path == r"s3://root/folder/output/data.zarr"
elif is_windows:
if platform.system() == "Windows":
assert joined_path == r"C:\root\folder\output\data.zarr"
else:
pytest.skip("Skipping Windows parameters because we are not on a Windows machine.")
else:
if platform.system() != "Windows":
assert joined_path == r"/root/folder/output/data.zarr"
else:
pytest.skip("Skipping Unix parameters because we are not on a Unix machine.")
@pytest.mark.parametrize(
("source_ds_da_input", "storage_options_input", "true_file_type"),
[
pytest.param(42, {}, None,
marks=pytest.mark.xfail(
strict=True,
reason='This test should fail because source_ds is not of the correct type.')
),
pytest.param(xr.DataArray(), {}, None),
pytest.param({}, 42, None,
marks=pytest.mark.xfail(
strict=True,
reason='This test should fail because storage_options is not of the correct type.')
),
(xr.Dataset(attrs={"test": 42}), {}, None),
(os.path.join('folder', 'new_test.nc'), {}, 'netcdf4'),
(os.path.join('folder', 'new_test.zarr'), {}, 'zarr')
]
)
def test_validate_source_ds_da(source_ds_da_input, storage_options_input, true_file_type):
"""
Tests that ``validate_source_ds_da`` has the appropriate outputs.
An exhaustive list of combinations of ``source_ds_da`` and ``storage_options``
are tested in ``test_validate_output_path`` and are therefore not included here.
"""
source_ds_output, file_type_output = validate_source_ds_da(source_ds_da_input, storage_options_input)
if isinstance(source_ds_da_input, (xr.Dataset, xr.DataArray)):
assert source_ds_output.identical(source_ds_da_input)
assert file_type_output is None
else:
assert isinstance(source_ds_output, str)
assert file_type_output == true_file_type
def test_init_ep_dir(monkeypatch):
temp_user_dir = tempfile.TemporaryDirectory()
echopype_dir = Path(temp_user_dir.name) / ".echopype"
# Create the .echopype in a temp dir instead of user space.
# Doing this will avoid accidentally deleting current
# working directory
monkeypatch.setattr(echopype.utils.io, "ECHOPYPE_DIR", echopype_dir)
assert echopype.utils.io.ECHOPYPE_DIR.exists() is False
init_ep_dir()
assert echopype.utils.io.ECHOPYPE_DIR.exists() is True
temp_user_dir.cleanup()
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,779 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py | from _echopype_version import version as __version__ # noqa
from .v05x_to_v06x import convert_v05x_to_v06x
def map_ep_version(echodata_obj):
"""
Function that coordinates the conversion between echopype versions
Parameters
----------
echodata_obj : EchoData
EchoData object that may need to be converted
Notes
-----
The function directly modifies the input EchoData object.
"""
if (0, 5, 0) <= echodata_obj.version_info < (0, 6, 0):
convert_v05x_to_v06x(echodata_obj)
elif (0, 6, 0) <= echodata_obj.version_info < (0, 8, 0):
pass
else:
str_version = ".".join(map(str, echodata_obj.version_info))
raise NotImplementedError(
f"Conversion of data from echopype v{str_version} format to"
+ f" v{__version__} format is not available. Please use open_raw"
+ f" to convert data to version {__version__} format."
)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": 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"/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,780 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/mask/test_mask.py | import pathlib
import pytest
import numpy as np
import xarray as xr
import dask.array
import tempfile
import os
import echopype as ep
import echopype.mask
from echopype.mask.api import (
_check_source_Sv_freq_diff,
_validate_and_collect_mask_input,
_check_var_name_fill_value
)
from typing import List, Union, Optional
def get_mock_freq_diff_data(n: int, n_chan_freq: int, add_chan: bool,
add_freq_nom: bool) -> xr.Dataset:
"""
Creates an in-memory mock Sv Dataset.
Parameters
----------
n: int
The number of rows (``ping_time``) and columns (``range_sample``) of
each channel matrix
n_chan_freq: int
Determines the size of the ``channel`` coordinate and ``frequency_nominal``
variable. To create mock data with known outcomes for ``frequency_differencing``,
this value must be greater than or equal to 3.
add_chan: bool
If True the ``channel`` dimension will be named "channel", else it will
be named "data_coord"
add_freq_nom: bool
If True the ``frequency_nominal`` variable will be added to the Dataset
Returns
-------
mock_Sv_ds: xr.Dataset
A mock Sv dataset to be used for ``frequency_differencing`` tests. The Sv
data values for the channel coordinate ``chan1`` will be equal to ``mat_A``,
``chan3`` will be equal to ``mat_B``, and all other channel coordinates
will retain the value of ``np.identity(n)``.
Notes
-----
The mock Sv Data is created in such a way where ``mat_A - mat_B`` will be
the identity matrix.
"""
if n_chan_freq < 3:
raise RuntimeError("The input n_chan_freq must be greater than or equal to 3!")
# matrix representing freqB
mat_B = np.arange(n ** 2).reshape(n, n) - np.identity(n)
# matrix representing freqA
mat_A = np.arange(n ** 2).reshape(n, n)
# construct channel values
chan_vals = ['chan' + str(i) for i in range(1, n_chan_freq + 1)]
# construct mock Sv data
mock_Sv_data = [mat_A, np.identity(n), mat_B] + [np.identity(n) for i in range(3, n_chan_freq)]
# set channel coordinate name (used for testing purposes)
if not add_chan:
channel_coord_name = "data_coord"
else:
channel_coord_name = "channel"
# create mock Sv DataArray
mock_Sv_da = xr.DataArray(data=np.stack(mock_Sv_data),
coords={channel_coord_name: chan_vals, "ping_time": np.arange(n),
"range_sample": np.arange(n)})
# create data variables for the Dataset
data_vars = {"Sv": mock_Sv_da}
if add_freq_nom:
# construct frequency_values
freq_vals = [float(i) for i in range(1, n_chan_freq + 1)]
# create mock frequency_nominal and add it to the Dataset variables
mock_freq_nom = xr.DataArray(data=freq_vals, coords={channel_coord_name: chan_vals})
data_vars["frequency_nominal"] = mock_freq_nom
# create mock Dataset with Sv and frequency_nominal
mock_Sv_ds = xr.Dataset(data_vars=data_vars)
return mock_Sv_ds
def get_mock_source_ds_apply_mask(n: int, n_chan: int, is_delayed: bool) -> xr.Dataset:
"""
Constructs a mock ``source_ds`` Dataset input for the
``apply_mask`` function.
Parameters
----------
n: int
The ``ping_time`` and ``range_sample`` dimensions of each channel matrix
n_chan: int
Size of the ``channel`` coordinate
is_delayed: bool
If True, the returned Dataset variables ``var1`` and ``var2`` will be
a Dask arrays, else they will be in-memory arrays
Returns
-------
xr.Dataset
A Dataset containing data variables ``var1, var2`` with coordinates
``('channel', 'ping_time', 'range_sample')``.
The variables are square matrices of ones for each ``channel``.
"""
# construct channel values
chan_vals = ['chan' + str(i) for i in range(1, n_chan + 1)]
# construct mock variable data for each channel
if is_delayed:
mock_var_data = [dask.array.ones((n, n)) for i in range(n_chan)]
else:
mock_var_data = [np.ones((n, n)) for i in range(n_chan)]
# create mock var1 and var2 DataArrays
mock_var1_da = xr.DataArray(data=np.stack(mock_var_data),
coords={"channel": ("channel", chan_vals, {"long_name": "channel name"}),
"ping_time": np.arange(n), "range_sample": np.arange(n)},
attrs={"long_name": "variable 1"})
mock_var2_da = xr.DataArray(data=np.stack(mock_var_data),
coords={"channel": ("channel", chan_vals, {"long_name": "channel name"}),
"ping_time": np.arange(n),
"range_sample": np.arange(n)},
attrs={"long_name": "variable 2"})
# create mock Dataset
mock_ds = xr.Dataset(data_vars={"var1": mock_var1_da, "var2": mock_var2_da})
return mock_ds
def create_input_mask(
mask: Union[np.ndarray, List[np.ndarray]],
mask_file: Optional[Union[str, List[str]]],
mask_coords: Union[xr.core.coordinates.DataArrayCoordinates, dict],
):
"""
A helper function that correctly constructs the mask input, so it can be
used for ``apply_mask`` related tests.
Parameters
----------
mask: np.ndarray or list of np.ndarray
The mask(s) that should be applied to ``var_name``
mask_file: str or list of str, optional
If provided, the ``mask`` input will be written to a temporary directory
with file name ``mask_file``. This will then be used in ``apply_mask``.
mask_coords: xr.core.coordinates.DataArrayCoordinates or dict
The DataArray coordinates that should be used for each mask DataArray created
"""
# initialize temp_dir
temp_dir = None
# make input numpy array masks into DataArrays
if isinstance(mask, list):
# initialize final mask
mask_out = []
# create temporary directory if mask_file is provided
if any([isinstance(elem, str) for elem in mask_file]):
# create temporary directory for mask_file
temp_dir = tempfile.TemporaryDirectory()
for mask_ind in range(len(mask)):
# form DataArray from given mask data
mask_da = xr.DataArray(data=[mask[mask_ind]], coords=mask_coords, name='mask_' + str(mask_ind))
if mask_file[mask_ind] is None:
# set mask value to the DataArray given
mask_out.append(mask_da)
else:
# write DataArray to temporary directory
zarr_path = os.path.join(temp_dir.name, mask_file[mask_ind])
mask_da.to_dataset().to_zarr(zarr_path)
if isinstance(mask_file[mask_ind], pathlib.Path):
# make zarr_path into a Path object
zarr_path = pathlib.Path(zarr_path)
# set mask value to created path
mask_out.append(zarr_path)
elif isinstance(mask, np.ndarray):
# form DataArray from given mask data
mask_da = xr.DataArray(data=[mask], coords=mask_coords, name='mask_0')
if mask_file is None:
# set mask to the DataArray formed
mask_out = mask_da
else:
# create temporary directory for mask_file
temp_dir = tempfile.TemporaryDirectory()
# write DataArray to temporary directory
zarr_path = os.path.join(temp_dir.name, mask_file)
mask_da.to_dataset().to_zarr(zarr_path)
if isinstance(mask_file, pathlib.Path):
# make zarr_path into a Path object
zarr_path = pathlib.Path(zarr_path)
# set mask index to path
mask_out = zarr_path
return mask_out, temp_dir
@pytest.mark.parametrize(
("n", "n_chan_freq", "add_chan", "add_freq_nom", "freqAB", "chanAB"),
[
(5, 3, True, True, [1.0, 3.0], None),
(5, 3, True, True, None, ['chan1', 'chan3']),
pytest.param(5, 3, False, True, [1.0, 3.0], None,
marks=pytest.mark.xfail(strict=True,
reason="This should fail because the Dataset "
"will not have the channel coordinate.")),
pytest.param(5, 3, True, False, [1.0, 3.0], None,
marks=pytest.mark.xfail(strict=True,
reason="This should fail because the Dataset "
"will not have the frequency_nominal variable.")),
pytest.param(5, 3, True, True, [1.0, 4.0], None,
marks=pytest.mark.xfail(strict=True,
reason="This should fail because not all selected frequencies"
"are in the frequency_nominal variable.")),
pytest.param(5, 3, True, True, None, ['chan1', 'chan4'],
marks=pytest.mark.xfail(strict=True,
reason="This should fail because not all selected channels"
"are in the channel coordinate.")),
],
ids=["dataset_input_freqAB_provided", "dataset_input_chanAB_provided", "dataset_no_channel",
"dataset_no_frequency_nominal", "dataset_missing_freqAB_in_freq_nom",
"dataset_missing_chanAB_in_channel"]
)
def test_check_source_Sv_freq_diff(n: int, n_chan_freq: int, add_chan: bool, add_freq_nom: bool,
freqAB: List[float],
chanAB: List[str]):
"""
Test the inputs ``source_Sv, freqAB, chanAB`` for ``_check_source_Sv_freq_diff``.
Parameters
----------
n: int
The number of rows (``ping_time``) and columns (``range_sample``) of
each channel matrix
n_chan_freq: int
Determines the size of the ``channel`` coordinate and ``frequency_nominal``
variable. To create mock data with known outcomes for ``frequency_differencing``,
this value must be greater than or equal to 3.
add_chan: bool
If True the ``channel`` dimension will be named "channel", else it will
be named "data_coord"
add_freq_nom: bool
If True the ``frequency_nominal`` variable will be added to the Dataset
freqAB: list of float, optional
The pair of nominal frequencies to be used for frequency-differencing, where
the first element corresponds to ``freqA`` and the second element corresponds
to ``freqB``
chanAB: list of float, optional
The pair of channels that will be used to select the nominal frequencies to be
used for frequency-differencing, where the first element corresponds to ``freqA``
and the second element corresponds to ``freqB``
"""
source_Sv = get_mock_freq_diff_data(n, n_chan_freq, add_chan, add_freq_nom)
_check_source_Sv_freq_diff(source_Sv, freqAB=freqAB, chanAB=chanAB)
@pytest.mark.parametrize(
("n", "n_chan_freq", "freqAB", "chanAB", "diff", "operator", "mask_truth"),
[
(5, 4, [1.0, 3.0], None, 1.0, "==", np.identity(5)),
(5, 4, None, ['chan1', 'chan3'], 1.0, "==", np.identity(5)),
(5, 4, [3.0, 1.0], None, 1.0, "==", np.zeros((5, 5))),
(5, 4, None, ['chan3', 'chan1'], 1.0, "==", np.zeros((5, 5))),
(5, 4, [1.0, 3.0], None, 1.0, ">=", np.identity(5)),
(5, 4, None, ['chan1', 'chan3'], 1.0, ">=", np.identity(5)),
(5, 4, [1.0, 3.0], None, 1.0, ">", np.zeros((5, 5))),
(5, 4, None, ['chan1', 'chan3'], 1.0, ">", np.zeros((5, 5))),
(5, 4, [1.0, 3.0], None, 1.0, "<=", np.ones((5, 5))),
(5, 4, None, ['chan1', 'chan3'], 1.0, "<=", np.ones((5, 5))),
(5, 4, [1.0, 3.0], None, 1.0, "<", np.ones((5, 5)) - np.identity(5)),
(5, 4, None, ['chan1', 'chan3'], 1.0, "<", np.ones((5, 5)) - np.identity(5)),
],
ids=["freqAB_sel_op_equals", "chanAB_sel_op_equals", "reverse_freqAB_sel_op_equals",
"reverse_chanAB_sel_op_equals", "freqAB_sel_op_ge", "chanAB_sel_op_ge",
"freqAB_sel_op_greater", "chanAB_sel_op_greater", "freqAB_sel_op_le",
"chanAB_sel_op_le", "freqAB_sel_op_less", "chanAB_sel_op_less"]
)
def test_frequency_differencing(n: int, n_chan_freq: int,
freqAB: List[float], chanAB: List[str],
diff: Union[float, int], operator: str,
mask_truth: np.ndarray):
"""
Tests that the output values of ``frequency_differencing`` are what we
expect, the output is a DataArray, and that the name of the DataArray is correct.
Parameters
----------
n: int
The number of rows (``ping_time``) and columns (``range_sample``) of
each channel matrix
n_chan_freq: int
Determines the size of the ``channel`` coordinate and ``frequency_nominal``
variable. To create mock data with known outcomes for ``frequency_differencing``,
this value must be greater than or equal to 3.
freqAB: list of float, optional
The pair of nominal frequencies to be used for frequency-differencing, where
the first element corresponds to ``freqA`` and the second element corresponds
to ``freqB``
chanAB: list of float, optional
The pair of channels that will be used to select the nominal frequencies to be
used for frequency-differencing, where the first element corresponds to ``freqA``
and the second element corresponds to ``freqB``
diff: float or int
The threshold of Sv difference between frequencies
operator: {">", "<", "<=", ">=", "=="}
The operator for the frequency-differencing
mask_truth: np.ndarray
The truth value for the output mask, provided the given inputs
"""
# obtain mock Sv Dataset
mock_Sv_ds = get_mock_freq_diff_data(n, n_chan_freq, add_chan=True, add_freq_nom=True)
# obtain the frequency-difference mask for mock_Sv_ds
out = ep.mask.frequency_differencing(source_Sv=mock_Sv_ds, storage_options={}, freqAB=freqAB,
chanAB=chanAB,
operator=operator, diff=diff)
# ensure that the output values are correct
assert np.all(out == mask_truth)
# ensure that the output is a DataArray
assert isinstance(out, xr.DataArray)
# test that the output DataArray is correctly names
assert out.name == "mask"
@pytest.mark.parametrize(
("n", "n_chan", "mask_np", "mask_file", "storage_options_mask"),
[
(5, 1, np.identity(5), None, {}),
(5, 1, [np.identity(5), np.identity(5)], [None, None], {}),
(5, 1, [np.identity(5), np.identity(5)], [None, None], [{}, {}]),
(5, 1, np.identity(5), "path/to/mask.zarr", {}),
(5, 1, [np.identity(5), np.identity(5)], ["path/to/mask0.zarr", "path/to/mask1.zarr"], {}),
(5, 1, np.identity(5), pathlib.Path("path/to/mask.zarr"), {}),
(5, 1, [np.identity(5), np.identity(5), np.identity(5)],
[None, "path/to/mask0.zarr", pathlib.Path("path/to/mask1.zarr")], {})
],
ids=["mask_da", "mask_list_da_single_storage", "mask_list_da_list_storage", "mask_str_path",
"mask_list_str_path", "mask_pathlib", "mask_mixed_da_str_pathlib"]
)
def test_validate_and_collect_mask_input(
n: int,
n_chan: int,
mask_np: Union[np.ndarray, List[np.ndarray]],
mask_file: Optional[Union[str, pathlib.Path, List[Union[str, pathlib.Path]]]],
storage_options_mask: Union[dict, List[dict]]):
"""
Tests the allowable types for the mask input and corresponding storage options.
Parameters
----------
n: int
The number of rows (``x``) and columns (``y``) of
each channel matrix
n_chan: int
Determines the size of the ``channel`` coordinate
mask_np: np.ndarray or list of np.ndarray
The mask(s) that should be applied to ``var_name``
mask_file: str or list of str, optional
If provided, the ``mask`` input will be written to a temporary directory
with file name ``mask_file``. This will then be used in ``apply_mask``.
storage_options_mask: dict or list of dict, default={}
Any additional parameters for the storage backend, corresponding to the
path provided for ``mask``
Notes
-----
The input for ``storage_options_mask`` will only contain the value `{}` or a list of
empty dictionaries as other options are already tested in
``test_utils_io.py::test_validate_output_path`` and are therefore not included here.
"""
# construct channel values
chan_vals = ['chan' + str(i) for i in range(1, n_chan + 1)]
# create coordinates that will be used by all DataArrays created
coords = {"channel": ("channel", chan_vals, {"long_name": "channel name"}),
"ping_time": np.arange(n), "range_sample": np.arange(n)}
# create input mask and obtain temporary directory, if it was created
mask, _ = create_input_mask(mask_np, mask_file, coords)
mask_out = _validate_and_collect_mask_input(mask=mask, storage_options_mask=storage_options_mask)
if isinstance(mask_out, list):
for ind, da in enumerate(mask_out):
# create known solution for mask
mask_da = xr.DataArray(data=[mask_np[ind] for i in range(n_chan)],
coords=coords, name='mask_' + str(ind))
assert da.identical(mask_da)
else:
# create known solution for mask
mask_da = xr.DataArray(data=[mask_np for i in range(n_chan)],
coords=coords, name='mask_0')
assert mask_out.identical(mask_da)
@pytest.mark.parametrize(
("n", "n_chan", "var_name", "fill_value"),
[
pytest.param(4, 2, 2.0, np.nan,
marks=pytest.mark.xfail(strict=True,
reason="This should fail because the var_name is not a string.")),
pytest.param(4, 2, "var3", np.nan,
marks=pytest.mark.xfail(strict=True,
reason="This should fail because mock_ds will "
"not have var_name=var3 in it.")),
pytest.param(4, 2, "var1", "1.0",
marks=pytest.mark.xfail(strict=True,
reason="This should fail because fill_value is an incorrect type.")),
(4, 2, "var1", 1),
(4, 2, "var1", 1.0),
(2, 1, "var1", np.identity(2)[None, :]),
(2, 1, "var1", xr.DataArray(data=np.array([[[1.0, 0], [0, 1]]]),
coords={"channel": ["chan1"], "ping_time": [0, 1], "range_sample": [0, 1]})
),
pytest.param(4, 2, "var1", np.identity(2),
marks=pytest.mark.xfail(strict=True,
reason="This should fail because fill_value is not the right shape.")),
pytest.param(4, 2, "var1",
xr.DataArray(data=np.array([[1.0, 0], [0, 1]]),
coords={"ping_time": [0, 1], "range_sample": [0, 1]}),
marks=pytest.mark.xfail(strict=True,
reason="This should fail because fill_value is not the right shape.")),
],
ids=["wrong_var_name_type", "no_var_name_ds", "wrong_fill_value_type", "fill_value_int",
"fill_value_float", "fill_value_np_array", "fill_value_DataArray",
"fill_value_np_array_wrong_shape", "fill_value_DataArray_wrong_shape"]
)
def test_check_var_name_fill_value(n: int, n_chan: int, var_name: str,
fill_value: Union[int, float, np.ndarray, xr.DataArray]):
"""
Ensures that the function ``_check_var_name_fill_value`` is behaving as expected.
Parameters
----------
n: int
The number of rows (``x``) and columns (``y``) of
each channel matrix
n_chan: int
Determines the size of the ``channel`` coordinate
var_name: {"var1", "var2"}
The variable name in the mock Dataset to apply the mask to
fill_value: int, float, np.ndarray, or xr.DataArray
Value(s) at masked indices
"""
# obtain mock Dataset containing var_name
mock_ds = get_mock_source_ds_apply_mask(n, n_chan, is_delayed=False)
_check_var_name_fill_value(source_ds=mock_ds, var_name=var_name, fill_value=fill_value)
@pytest.mark.parametrize(
("n", "n_chan", "var_name", "mask", "mask_file", "fill_value", "is_delayed", "var_masked_truth", "no_channel"),
[
# single_mask_default_fill
(2, 1, "var1", np.identity(2), None, np.nan, False, np.array([[1, np.nan], [np.nan, 1]]), False),
# single_mask_default_fill_no_channel
(2, 1, "var1", np.identity(2), None, np.nan, False, np.array([[1, np.nan], [np.nan, 1]]), True),
# single_mask_float_fill
(2, 1, "var1", np.identity(2), None, 2.0, False, np.array([[1, 2.0], [2.0, 1]]), False),
# single_mask_np_array_fill
(2, 1, "var1", np.identity(2), None, np.array([[[np.nan, np.nan], [np.nan, np.nan]]]),
False, np.array([[1, np.nan], [np.nan, 1]]), False),
# single_mask_DataArray_fill
(2, 1, "var1", np.identity(2), None, xr.DataArray(data=np.array([[[np.nan, np.nan], [np.nan, np.nan]]]),
coords={"channel": ["chan1"],
"ping_time": [0, 1],
"range_sample": [0, 1]}),
False, np.array([[1, np.nan], [np.nan, 1]]), False),
# list_mask_all_np
(2, 1, "var1", [np.identity(2), np.array([[0, 1], [0, 1]])], [None, None], 2.0,
False, np.array([[2.0, 2.0], [2.0, 1]]), False),
# single_mask_ds_delayed
(2, 1, "var1", np.identity(2), None, 2.0, True, np.array([[1, 2.0], [2.0, 1]]), False),
# single_mask_as_path
(2, 1, "var1", np.identity(2), "test.zarr", 2.0, True, np.array([[1, 2.0], [2.0, 1]]), False),
# list_mask_all_path
(2, 1, "var1", [np.identity(2), np.array([[0, 1], [0, 1]])], ["test0.zarr", "test1.zarr"], 2.0,
False, np.array([[2.0, 2.0], [2.0, 1]]), False),
# list_mask_some_path
(2, 1, "var1", [np.identity(2), np.array([[0, 1], [0, 1]])], ["test0.zarr", None], 2.0,
False, np.array([[2.0, 2.0], [2.0, 1]]), False),
],
ids=[
"single_mask_default_fill",
"single_mask_default_fill_no_channel",
"single_mask_float_fill",
"single_mask_np_array_fill",
"single_mask_DataArray_fill",
"list_mask_all_np",
"single_mask_ds_delayed",
"single_mask_as_path",
"list_mask_all_path",
"list_mask_some_path"
]
)
def test_apply_mask(n: int, n_chan: int, var_name: str,
mask: Union[np.ndarray, List[np.ndarray]],
mask_file: Optional[Union[str, List[str]]],
fill_value: Union[int, float, np.ndarray, xr.DataArray],
is_delayed: bool,
var_masked_truth: np.ndarray,
no_channel: bool):
"""
Ensures that ``apply_mask`` functions correctly.
Parameters
----------
n: int
The number of rows (``x``) and columns (``y``) of
each channel matrix
n_chan: int
Determines the size of the ``channel`` coordinate
var_name: {"var1", "var2"}
The variable name in the mock Dataset to apply the mask to
mask: np.ndarray or list of np.ndarray
The mask(s) that should be applied to ``var_name``
mask_file: str or list of str, optional
If provided, the ``mask`` input will be written to a temporary directory
with file name ``mask_file``. This will then be used in ``apply_mask``.
fill_value: int, float, np.ndarray, or xr.DataArray
Value(s) at masked indices
var_masked_truth: np.ndarray
The true value of ``var_name`` values after the mask has been applied
is_delayed: bool
If True, makes all variables in constructed mock Dataset Dask arrays,
else they will be in-memory arrays
"""
# obtain mock Dataset containing var_name
mock_ds = get_mock_source_ds_apply_mask(n, n_chan, is_delayed)
# create input mask and obtain temporary directory, if it was created
mask, temp_dir = create_input_mask(mask, mask_file, mock_ds.coords)
# create DataArray form of the known truth value
var_masked_truth = xr.DataArray(data=np.stack([var_masked_truth for i in range(n_chan)]),
coords=mock_ds[var_name].coords, attrs=mock_ds[var_name].attrs)
var_masked_truth.name = mock_ds[var_name].name
if no_channel:
mock_ds = mock_ds.isel(channel=0)
mask = mask.isel(channel=0)
var_masked_truth = var_masked_truth.isel(channel=0)
# apply the mask to var_name
masked_ds = echopype.mask.apply_mask(source_ds=mock_ds, var_name=var_name, mask=mask,
fill_value=fill_value, storage_options_ds={},
storage_options_mask={})
# check that masked_ds[var_name] == var_masked_truth
assert masked_ds[var_name].equals(var_masked_truth)
# check that the output Dataset has lazy elements, if the input was lazy
if is_delayed:
assert isinstance(masked_ds[var_name].data, dask.array.Array)
if temp_dir:
# remove the temporary directory, if it was created
temp_dir.cleanup()
@pytest.mark.parametrize(
("source_has_ch", "mask_has_ch"),
[
(True, True),
(False, True),
(True, False),
(False, False),
],
ids=[
"source_with_ch_mask_with_ch",
"source_no_ch_mask_with_ch",
"source_with_ch_mask_no_ch",
"source_no_ch_mask_no_ch",
]
)
def test_apply_mask_channel_variation(source_has_ch, mask_has_ch):
source_ds = get_mock_source_ds_apply_mask(2, 3, False)
var_name = "var1"
if mask_has_ch:
mask = xr.DataArray(
np.array([np.identity(2)]),
coords={"channel": ["chA"], "ping_time": np.arange(2), "range_sample": np.arange(2)},
attrs={"long_name": "mask_with_channel"},
)
else:
mask = xr.DataArray(
np.identity(2),
coords={"ping_time": np.arange(2), "range_sample": np.arange(2)},
attrs={"long_name": "mask_no_channel"},
)
if source_has_ch:
masked_ds = echopype.mask.apply_mask(source_ds, mask, var_name)
else:
source_ds[f"{var_name}_ch0"] = source_ds[var_name].isel(channel=0).squeeze()
var_name = f"{var_name}_ch0"
masked_ds = echopype.mask.apply_mask(source_ds, mask, var_name)
# Output dimension will be the same as source
if source_has_ch:
truth_da = xr.DataArray(
np.array([[[1, np.nan], [np.nan, 1]]] * 3),
coords={"channel": ["chan1", "chan2", "chan3"], "ping_time": np.arange(2), "range_sample": np.arange(2)},
attrs=source_ds[var_name].attrs
)
else:
truth_da = xr.DataArray(
[[1, np.nan], [np.nan, 1]],
coords={"ping_time": np.arange(2), "range_sample": np.arange(2)},
attrs=source_ds[var_name].attrs
)
assert masked_ds[var_name].equals(truth_da) | {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,781 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/visualize/cm.py | import numpy as np
import matplotlib as mpl
__cmap_colors = {
'ek500': {
'rgb': (
np.array(
[
[159, 159, 159], # light grey
[95, 95, 95], # grey
[0, 0, 255], # dark blue
[0, 0, 127], # blue
[0, 191, 0], # green
[0, 127, 0], # dark green
[255, 255, 0], # yellow
[255, 127, 0], # orange
[255, 0, 191], # pink
[255, 0, 0], # red
[166, 83, 60], # light brown
]
)
/ 255
),
'under': '1', # white
'over': np.array([120, 60, 40]) / 255, # dark brown
}
}
def _create_cmap(rgb, under=None, over=None):
cmap = mpl.colors.ListedColormap(rgb)
if under is not None:
cmap.set_under(under)
if over is not None:
cmap.set_over(over)
return cmap
cmap_d = {}
cmapnames = ['ek500']
# add colormaps and reversed to dictionary
for cmapname in cmapnames:
colors_d = __cmap_colors[cmapname]
rgb = colors_d['rgb']
cmap_d[cmapname] = _create_cmap(
rgb, under=colors_d.get('under', None), over=colors_d.get('over', None)
)
cmap_d[cmapname].name = cmapname
cmap_d[cmapname + '_r'] = _create_cmap(
rgb[::-1, :],
under=colors_d.get('over', None),
over=colors_d.get('under', None),
)
cmap_d[cmapname + '_r'].name = cmapname + '_r'
# Register the cmap with matplotlib
rgb_with_alpha = np.zeros((rgb.shape[0], 4))
rgb_with_alpha[:, :3] = rgb
rgb_with_alpha[:, 3] = 1.0 # set alpha channel to 1
reg_map = mpl.colors.ListedColormap(
rgb_with_alpha, 'ep.' + cmapname, rgb.shape[0]
)
if 'under' in colors_d:
reg_map.set_under(colors_d['under'])
if 'over' in colors_d:
reg_map.set_over(colors_d['over'])
mpl.colormaps.register(cmap=reg_map)
# Register the reversed map
reg_map_r = mpl.colors.ListedColormap(
rgb_with_alpha[::-1, :], 'ep.' + cmapname + '_r', rgb.shape[0]
)
if 'under' in colors_d:
reg_map_r.set_over(colors_d['under'])
if 'over' in colors_d:
reg_map_r.set_under(colors_d['over'])
mpl.colormaps.register(cmap=reg_map_r)
# make colormaps available to call
locals().update(cmap_d)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,782 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/conftest.py | """``pytest`` configuration."""
import pytest
import fsspec
from echopype.testing import TEST_DATA_FOLDER
@pytest.fixture(scope="session")
def dump_output_dir():
return TEST_DATA_FOLDER / "dump"
@pytest.fixture(scope="session")
def test_path():
return {
'ROOT': TEST_DATA_FOLDER,
'EA640': TEST_DATA_FOLDER / "ea640",
'EK60': TEST_DATA_FOLDER / "ek60",
'EK80': TEST_DATA_FOLDER / "ek80",
'EK80_NEW': TEST_DATA_FOLDER / "ek80_new",
'ES70': TEST_DATA_FOLDER / "es70",
'ES80': TEST_DATA_FOLDER / "es80",
'AZFP': TEST_DATA_FOLDER / "azfp",
'AD2CP': TEST_DATA_FOLDER / "ad2cp",
'EK80_CAL': TEST_DATA_FOLDER / "ek80_bb_with_calibration",
'EK80_EXT': TEST_DATA_FOLDER / "ek80_ext",
'ECS': TEST_DATA_FOLDER / "ecs",
}
@pytest.fixture(scope="session")
def minio_bucket():
return dict(
client_kwargs=dict(endpoint_url="http://localhost:9000/"),
key="minioadmin",
secret="minioadmin",
)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,783 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/echodata/utils.py | import os
import json
from pathlib import Path
import xarray as xr
from datatree import DataTree
import numpy as np
from echopype.convert.set_groups_base import SetGroupsBase
from echopype.echodata.echodata import EchoData
class SetGroupsTest(SetGroupsBase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def set_beam(self) -> xr.Dataset:
ds = xr.Dataset(
attrs={"beam_mode": "vertical", "conversion_equation_t": "type_3"}
)
return ds
def set_env(self) -> xr.Dataset:
# TODO: add mock data
ds = xr.Dataset()
env_attr_dict = {
"notes": "This is a mock env dataset, hence no data is found!"
}
ds = ds.assign_attrs(env_attr_dict)
return ds
def set_platform(self) -> xr.Dataset:
# TODO: add mock data
ds = xr.Dataset(
attrs={
"platform_code_ICES": 315,
"platform_name": "My mock boat",
"platform_type": "Research vessel",
}
)
return ds
def set_nmea(self) -> xr.Dataset:
# TODO: add mock data
ds = xr.Dataset(
attrs={
"description": "All Mock NMEA datagrams",
}
)
return ds
def set_sonar(self) -> xr.Dataset:
# TODO: add mock data
ds = xr.Dataset()
# Assemble sonar group global attribute dictionary
sonar_attr_dict = {
"sonar_manufacturer": "Simrad",
"sonar_model": self.sonar_model,
# transducer (sonar) serial number is not stored in the EK60 raw data file,
# so sonar_serial_number can't be populated from the raw datagrams
"sonar_serial_number": "",
"sonar_software_name": "",
"sonar_software_version": "0.1.0",
"sonar_type": "echosounder",
}
ds = ds.assign_attrs(sonar_attr_dict)
return ds
def set_vendor(self) -> xr.Dataset:
# TODO: add mock data
ds = xr.Dataset(attrs={"created_by": "Mock test"})
return ds
def get_mock_echodata(
sonar_model='TEST',
file_chk='./test.raw',
xml_chk=None,
):
# Setup tree dictionary
tree_dict = {}
setgrouper = SetGroupsTest(
parser_obj=None,
input_file=file_chk,
xml_path=xml_chk,
output_path=None,
sonar_model=sonar_model,
params={"survey_name": "mock_survey"},
)
tree_dict["/"] = setgrouper.set_toplevel(
sonar_model, date_created=np.datetime64("1970-01-01")
)
tree_dict["Environment"] = setgrouper.set_env()
tree_dict["Platform"] = setgrouper.set_platform()
tree_dict["Platform/NMEA"] = setgrouper.set_nmea()
tree_dict["Provenance"] = setgrouper.set_provenance()
tree_dict["Sonar"] = None
tree_dict["Sonar/Beam_group1"] = setgrouper.set_beam()
tree_dict["Sonar"] = setgrouper.set_sonar()
tree_dict["Vendor_specific"] = setgrouper.set_vendor()
tree = DataTree.from_dict(tree_dict, name="root")
echodata = EchoData(
source_file=file_chk, xml_path=xml_chk, sonar_model=sonar_model
)
echodata._set_tree(tree)
echodata._load_tree()
return echodata
def check_consolidated(echodata: EchoData, zmeta_path: Path) -> None:
"""
Checks for the presence of `.zgroup`
for every group in echodata within the `.zmetadata`
file.
Parameters
----------
echodata : EchoData
The echodata object to be checked.
zmeta_path : pathlib.Path
The path to the .zmetadata for the zarr file.
"""
# Check that every group is in
# the zmetadata if consolidated
expected_zgroups = [
os.path.join(p, '.zgroup') if p != 'Top-level' else '.zgroup'
for p in echodata.group_paths
]
with open(zmeta_path) as f:
meta_json = json.load(f)
file_groups = [
k
for k in meta_json['metadata'].keys()
if k.endswith('.zgroup')
]
for g in expected_zgroups:
assert g in file_groups, f"{g} not Found!"
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,784 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/commongrid/test_mvbs.py | import dask.array
import numpy as np
from numpy.random import default_rng
import pandas as pd
import pytest
from typing import Tuple, Iterable, Union
import xarray as xr
import echopype as ep
from echopype.commongrid.mvbs import bin_and_mean_2d
@pytest.fixture(
params=[
(
("EK60", "ncei-wcsd", "Summer2017-D20170719-T211347.raw"),
"EK60",
None,
{},
),
(
("EK80_NEW", "echopype-test-D20211004-T235930.raw"),
"EK80",
None,
{'waveform_mode': 'BB', 'encode_mode': 'complex'},
),
(
("EK80_NEW", "D20211004-T233354.raw"),
"EK80",
None,
{'waveform_mode': 'CW', 'encode_mode': 'power'},
),
(
("EK80_NEW", "D20211004-T233115.raw"),
"EK80",
None,
{'waveform_mode': 'CW', 'encode_mode': 'complex'},
),
(("ES70", "D20151202-T020259.raw"), "ES70", None, {}),
(("AZFP", "17082117.01A"), "AZFP", ("AZFP", "17041823.XML"), {}),
(
("AD2CP", "raw", "090", "rawtest.090.00001.ad2cp"),
"AD2CP",
None,
{},
),
],
ids=[
"ek60_cw_power",
"ek80_bb_complex",
"ek80_cw_power",
"ek80_cw_complex",
"es70",
"azfp",
"ad2cp",
],
)
def test_data_samples(request, test_path):
(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
) = request.param
if sonar_model.lower() in ['es70', 'ad2cp']:
pytest.xfail(
reason="Not supported at the moment",
)
path_model, *paths = filepath
filepath = test_path[path_model].joinpath(*paths)
if azfp_xml_path is not None:
path_model, *paths = azfp_xml_path
azfp_xml_path = test_path[path_model].joinpath(*paths)
return (
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
)
def _construct_MVBS_toy_data(
nchan, npings, nrange_samples, ping_size, range_sample_size
):
"""Construct data with values that increase every ping_num and ``range_sample_num``
so that the result of computing MVBS is a smaller array
that increases regularly for each resampled ``ping_time`` and ``range_sample``
Parameters
----------
nchan : int
number of channels
npings : int
number of pings
nrange_samples : int
number of range samples
ping_size : int
number of pings with the same value
range_sample_size : int
number of range samples with the same value
Returns
-------
np.ndarray
Array with blocks of ``ping_time`` and ``range_sample`` with the same value,
so that computing the MVBS will result in regularly increasing values
every row and column
"""
data = np.ones((nchan, npings, nrange_samples))
for p_i, ping in enumerate(range(0, npings, ping_size)):
for r_i, rb in enumerate(range(0, nrange_samples, range_sample_size)):
data[0, ping : ping + ping_size, rb : rb + range_sample_size] += (
r_i + p_i
)
# First channel increases by 1 each row and column, second increases by 2, third by 3, etc.
for f in range(nchan):
data[f] = data[0] * (f + 1)
return data
def _construct_MVBS_test_data(nchan, npings, nrange_samples):
"""Construct data for testing the toy data from
`_construct_MVBS_toy_data` after it has gone through the
MVBS calculation.
Parameters
----------
nchan : int
number of channels
npings : int
number of pings
nrange_samples : int
number of range samples
Returns
-------
np.ndarray
Array with values that increases regularly
every ping and range sample
"""
# Construct test array
test_array = np.add(*np.indices((npings, nrange_samples)))
return np.array([(test_array + 1) * (f + 1) for f in range(nchan)])
def test_compute_MVBS_index_binning():
"""Test compute_MVBS_index_binning on toy data"""
# Parameters for toy data
nchan, npings, nrange_samples = 4, 40, 400
ping_num = 3 # number of pings to average over
range_sample_num = 7 # number of range_samples to average over
# Construct toy data that increases regularly every ping_num and range_sample_num
data = _construct_MVBS_toy_data(
nchan=nchan,
npings=npings,
nrange_samples=nrange_samples,
ping_size=ping_num,
range_sample_size=range_sample_num,
)
data_log = 10 * np.log10(data) # Convert to log domain
chan_index = np.arange(nchan).astype(str)
ping_index = np.arange(npings)
range_sample = np.arange(nrange_samples)
Sv = xr.DataArray(
data_log,
coords=[
('channel', chan_index),
('ping_time', ping_index),
('range_sample', range_sample),
],
)
Sv.name = "Sv"
ds_Sv = Sv.to_dataset()
ds_Sv["frequency_nominal"] = chan_index # just so there's value in freq_nominal
ds_Sv = ds_Sv.assign(
echo_range=xr.DataArray(
np.array([[np.linspace(0, 10, nrange_samples)] * npings] * nchan),
coords=Sv.coords,
)
)
# Binned MVBS test
ds_MVBS = ep.commongrid.compute_MVBS_index_binning(
ds_Sv, range_sample_num=range_sample_num, ping_num=ping_num
)
data_test = 10 ** (ds_MVBS.Sv / 10) # Convert to linear domain
# Shape test
data_binned_shape = np.ceil(
(nchan, npings / ping_num, nrange_samples / range_sample_num)
).astype(int)
assert np.all(data_test.shape == data_binned_shape)
# Construct test array that increases by 1 for each range_sample and ping_time
test_array = _construct_MVBS_test_data(
nchan, data_binned_shape[1], data_binned_shape[2]
)
# Test all values in MVBS
assert np.allclose(data_test, test_array, rtol=0, atol=1e-12)
def _coll_test_comp_MVBS(ds_Sv, nchan, ping_num,
range_sample_num, ping_time_bin,
total_range, range_meter_bin):
"""A collection of tests for test_compute_MVBS"""
ds_MVBS = ep.commongrid.compute_MVBS(
ds_Sv,
range_meter_bin=range_meter_bin,
ping_time_bin=f'{ping_time_bin}S',
)
data_test = 10 ** (ds_MVBS.Sv / 10) # Convert to linear domain
# Shape test
data_binned_shape = np.ceil((nchan, ping_num, range_sample_num)).astype(int)
assert np.all(data_test.shape == data_binned_shape)
# Construct test array that increases by 1 for each range_sample and ping_time
test_array = _construct_MVBS_test_data(
nchan, data_binned_shape[1], data_binned_shape[2]
)
# Test all values in MVBS
assert np.allclose(data_test, test_array, rtol=0, atol=1e-12)
# Test to see if ping_time was resampled correctly
test_ping_time = pd.date_range(
'1/1/2020', periods=np.ceil(ping_num), freq=f'{ping_time_bin}S'
)
assert np.array_equal(data_test.ping_time, test_ping_time)
# Test to see if range was resampled correctly
test_range = np.arange(0, total_range, range_meter_bin)
assert np.array_equal(data_test.echo_range, test_range)
def _fill_w_nans(narr, nan_ping_time, nan_range_sample):
"""
A routine that fills a numpy array with nans.
Parameters
----------
narr : numpy array
Array of dimensions (ping_time, range_sample)
nan_ping_time : list
ping times to fill with nans
nan_range_sample: list
range samples to fill with nans
"""
if len(nan_ping_time) != len(nan_range_sample):
raise ValueError('These lists must be the same size!')
# fill in nans according to the provided lists
for i, j in zip(nan_ping_time, nan_range_sample):
narr[i, j] = np.nan
return narr
def _nan_cases_comp_MVBS(ds_Sv, chan):
"""
For a single channel, obtains numpy array
filled with nans for various cases
"""
# get echo_range values for a single channel
one_chan_er = ds_Sv.echo_range.sel(channel=chan).copy().values
# ping times to fill with NaNs
nan_ping_time_1 = [slice(None), slice(None)]
# range samples to fill with NaNs
nan_range_sample_1 = [3, 4]
# pad all ping_times with nans for a certain range_sample
case_1 = _fill_w_nans(one_chan_er, nan_ping_time_1, nan_range_sample_1)
# get echo_range values for a single channel
one_chan_er = ds_Sv.echo_range.sel(channel=chan).copy().values
# ping times to fill with NaNs
nan_ping_time_2 = [1, 3, 5, 9]
# range samples to fill with NaNs
nan_range_sample_2 = [slice(None), slice(None), slice(None), slice(None)]
# pad all range_samples of certain ping_times
case_2 = _fill_w_nans(one_chan_er, nan_ping_time_2, nan_range_sample_2)
# get echo_range values for a single channel
one_chan_er = ds_Sv.echo_range.sel(channel=chan).copy().values
# ping times to fill with NaNs
nan_ping_time_3 = [0, 2, 5, 7]
# range samples to fill with NaNs
nan_range_sample_3 = [slice(0, 2), slice(None), slice(None), slice(0, 3)]
# pad all range_samples of certain ping_times and
# pad some ping_times with nans for a certain range_sample
case_3 = _fill_w_nans(one_chan_er, nan_ping_time_3, nan_range_sample_3)
return case_1, case_2, case_3
def test_compute_MVBS():
"""Test compute_MVBS on toy data"""
# Parameters for fake data
nchan, npings, nrange_samples = 4, 100, 4000
range_meter_bin = 7 # range in meters to average over
ping_time_bin = 3 # number of seconds to average over
ping_rate = 2 # Number of pings per second
range_sample_per_meter = 30 # Number of range_samples per meter
# Useful conversions
ping_num = (
npings / ping_rate / ping_time_bin
) # number of pings to average over
range_sample_num = (
nrange_samples / range_sample_per_meter / range_meter_bin
) # number of range_samples to average over
total_range = nrange_samples / range_sample_per_meter # total range in meters
# Construct data with values that increase with range and time
# so that when compute_MVBS is performed, the result is a smaller array
# that increases by a constant for each meter_bin and time_bin
data = _construct_MVBS_toy_data(
nchan=nchan,
npings=npings,
nrange_samples=nrange_samples,
ping_size=ping_rate * ping_time_bin,
range_sample_size=range_sample_per_meter * range_meter_bin,
)
data_log = 10 * np.log10(data) # Convert to log domain
chan_index = np.arange(nchan).astype(str)
freq_nom = np.arange(nchan)
# Generate a date range with `npings` number of pings with the frequency of the ping_rate
ping_time = pd.date_range(
'1/1/2020', periods=npings, freq=f'{1/ping_rate}S'
)
range_sample = np.arange(nrange_samples)
Sv = xr.DataArray(
data_log,
coords=[
('channel', chan_index),
('ping_time', ping_time),
('range_sample', range_sample),
],
)
Sv.name = "Sv"
ds_Sv = Sv.to_dataset()
ds_Sv = ds_Sv.assign(
frequency_nominal=xr.DataArray(freq_nom, coords={'channel': chan_index}),
echo_range=xr.DataArray(
np.array(
[[np.linspace(0, total_range, nrange_samples)] * npings] * nchan
),
coords=Sv.coords,
)
)
# initial test of compute_MVBS
_coll_test_comp_MVBS(ds_Sv, nchan, ping_num,
range_sample_num, ping_time_bin,
total_range, range_meter_bin)
# TODO: use @pytest.fixture params/ids
# for multiple similar tests using the same set of parameters
# different nan cases for a single channel
case_1, case_2, case_3 = _nan_cases_comp_MVBS(ds_Sv, chan='0')
# pad all ping_times with nans for a certain range_sample
ds_Sv['echo_range'].loc[{'channel': '0'}] = case_1
_coll_test_comp_MVBS(ds_Sv, nchan, ping_num,
range_sample_num, ping_time_bin,
total_range, range_meter_bin)
# pad all range_samples of certain ping_times
ds_Sv['echo_range'].loc[{'channel': '0'}] = case_2
_coll_test_comp_MVBS(ds_Sv, nchan, ping_num,
range_sample_num, ping_time_bin,
total_range, range_meter_bin)
# pad all range_samples of certain ping_times and
# pad some ping_times with nans for a certain range_sample
ds_Sv['echo_range'].loc[{'channel': '0'}] = case_3
_coll_test_comp_MVBS(ds_Sv, nchan, ping_num,
range_sample_num, ping_time_bin,
total_range, range_meter_bin)
def test_commongrid_mvbs(test_data_samples):
"""
Test running through from open_raw to compute_MVBS.
"""
(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
) = test_data_samples
ed = ep.open_raw(filepath, sonar_model, azfp_xml_path)
if ed.sonar_model.lower() == 'azfp':
avg_temperature = ed["Environment"]['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
range_kwargs['env_params'] = env_params
if 'azfp_cal_type' in range_kwargs:
range_kwargs.pop('azfp_cal_type')
Sv = ep.calibrate.compute_Sv(ed, **range_kwargs)
assert ep.commongrid.compute_MVBS(Sv) is not None
def create_bins(csum_array: np.ndarray) -> Iterable:
"""
Constructs bin ranges based off of a cumulative
sum array.
Parameters
----------
csum_array: np.ndarray
1D array representing a cumulative sum
Returns
-------
bins: list
A list whose elements are the lower and upper bin ranges
"""
bins = []
# construct bins
for count, csum in enumerate(csum_array):
if count == 0:
bins.append([0, csum])
else:
# add 0.01 so that left bins don't overlap
bins.append([csum_array[count-1] + 0.01, csum])
return bins
def create_echo_range_related_data(ping_bins: Iterable,
num_pings_in_bin: np.ndarray,
er_range: list, er_bins: Iterable,
final_num_er_bins: int,
create_dask: bool,
rng: np.random.Generator,
ping_bin_nan_ind: int) -> Tuple[list, list, list]:
"""
Creates ``echo_range`` values and associated bin information.
Parameters
----------
ping_bins: list
A list whose elements are the lower and upper ping time bin ranges
num_pings_in_bin: np.ndarray
Specifies the number of pings in each ping time bin
er_range: list
A list whose first element is the lowest and second element is
the highest possible number of echo range values in a given bin
er_bins: list
A list whose elements are the lower and upper echo range bin ranges
final_num_er_bins: int
The total number of echo range bins
create_dask: bool
If True ``final_arrays`` values will be
dask arrays, else they will be numpy arrays
rng: np.random.Generator
The generator for random values
ping_bin_nan_ind: int
The ping bin index to fill with NaNs
Returns
-------
all_er_bin_nums: list of np.ndarray
A list whose elements are the number of values in each echo_range
bin, for each ping bin
ping_times_in_bin: list of np.ndarray
A list whose elements are the ping_time values for each corresponding bin
final_arrays: list of np.ndarray or dask.array.Array
A list whose elements are the echo_range values for a given ping and
echo range bin block
"""
final_arrays = []
all_er_bin_nums = []
ping_times_in_bin = []
# build echo_range array
for ping_ind, ping_bin in enumerate(ping_bins):
# create the ping times associated with each ping bin
ping_times_in_bin.append(rng.uniform(ping_bin[0], ping_bin[1], (num_pings_in_bin[ping_ind],)))
# randomly determine the number of values in each echo_range bin
num_er_in_bin = rng.integers(low=er_range[0], high=er_range[1], size=final_num_er_bins)
# store the number of values in each echo_range bin
all_er_bin_nums.append(num_er_in_bin)
er_row_block = []
for count, bin_val in enumerate(er_bins):
# create a block of echo_range values
if create_dask:
a = dask.array.random.uniform(bin_val[0], bin_val[1], (num_pings_in_bin[ping_ind],
num_er_in_bin[count]))
else:
a = rng.uniform(bin_val[0], bin_val[1], (num_pings_in_bin[ping_ind],
num_er_in_bin[count]))
# store the block of echo_range values
er_row_block.append(a)
# set all echo_range values at ping index to NaN
if ping_ind == ping_bin_nan_ind:
a[:, :] = np.nan
# collect and construct echo_range row block
final_arrays.append(np.concatenate(er_row_block, axis=1))
return all_er_bin_nums, ping_times_in_bin, final_arrays
def construct_2d_echo_range_array(final_arrays: Iterable[np.ndarray],
ping_csum: np.ndarray,
create_dask: bool) -> Tuple[Union[np.ndarray, dask.array.Array], int]:
"""
Creates the final 2D ``echo_range`` array with appropriate padding.
Parameters
----------
final_arrays: list of np.ndarray
A list whose elements are the echo_range values for a given ping and
echo range bin block
ping_csum: np.ndarray
1D array representing the cumulative sum for the number of ping times
in each ping bin
create_dask: bool
If True ``final_er`` will be a dask array, else it will be a numpy array
Returns
-------
final_er: np.ndarray or dask.array.Array
The final 2D ``echo_range`` array
max_num_er_elem: int
The maximum number of ``echo_range`` elements amongst all times
"""
# get maximum number of echo_range elements amongst all times
max_num_er_elem = max([arr.shape[1] for arr in final_arrays])
# total number of ping times
tot_num_times = ping_csum[-1]
# pad echo_range dimension with nans and create final echo_range
if create_dask:
final_er = dask.array.ones(shape=(tot_num_times, max_num_er_elem)) * np.nan
else:
final_er = np.empty((tot_num_times, max_num_er_elem))
final_er[:] = np.nan
for count, arr in enumerate(final_arrays):
if count == 0:
final_er[0:ping_csum[count], 0:arr.shape[1]] = arr
else:
final_er[ping_csum[count - 1]:ping_csum[count], 0:arr.shape[1]] = arr
return final_er, max_num_er_elem
def construct_2d_sv_array(max_num_er_elem: int, ping_csum: np.ndarray,
all_er_bin_nums: Iterable[np.ndarray],
num_pings_in_bin: np.ndarray,
create_dask: bool,
ping_bin_nan_ind: int) -> Tuple[Union[np.ndarray, dask.array.Array],
np.ndarray]:
"""
Creates the final 2D Sv array with appropriate padding.
Parameters
----------
max_num_er_elem: int
The maximum number of ``echo_range`` elements amongst all times
ping_csum: np.ndarray
1D array representing the cumulative sum for the number of ping times
in each ping bin
all_er_bin_nums: list of np.ndarray
A list whose elements are the number of values in each echo_range
bin, for each ping bin
num_pings_in_bin: np.ndarray
Specifies the number of pings in each ping time bin
create_dask: bool
If True ``final_sv`` will be a dask array, else it will be a numpy array
ping_bin_nan_ind: int
The ping bin index to fill with NaNs
Returns
-------
final_sv: np.ndarray or dask.array.Array
The final 2D Sv array
final_MVBS: np.ndarray
The final 2D known MVBS array
"""
# total number of ping times
tot_num_times = ping_csum[-1]
# pad echo_range dimension with nans and create final sv
if create_dask:
final_sv = dask.array.ones(shape=(tot_num_times, max_num_er_elem)) * np.nan
else:
final_sv = np.empty((tot_num_times, max_num_er_elem))
final_sv[:] = np.nan
final_means = []
for count, arr in enumerate(all_er_bin_nums):
# create sv row values using natural numbers
sv_row_list = [np.arange(1, num_elem + 1, 1, dtype=np.float64) for num_elem in arr]
# create final sv row
sv_row = np.concatenate(sv_row_list)
# get final mean which is n+1/2 (since we are using natural numbers)
ping_mean = [(len(elem) + 1) / 2.0 for elem in sv_row_list]
# create sv row block
sv_row_block = np.tile(sv_row, (num_pings_in_bin[count], 1))
if count == ping_bin_nan_ind:
# fill values with NaNs
ping_mean = [np.nan]*len(sv_row_list)
sv_row_block[:, :] = np.nan
# store means for ping
final_means.append(ping_mean)
if count == 0:
final_sv[0:ping_csum[count], 0:sv_row_block.shape[1]] = sv_row_block
else:
final_sv[ping_csum[count - 1]:ping_csum[count], 0:sv_row_block.shape[1]] = sv_row_block
# create final sv MVBS
final_MVBS = np.vstack(final_means)
return final_sv, final_MVBS
def create_known_mean_data(final_num_ping_bins: int,
final_num_er_bins: int,
ping_range: list,
er_range: list, create_dask: bool,
rng: np.random.Generator) -> Tuple[np.ndarray, np.ndarray, Iterable,
Iterable, np.ndarray, np.ndarray]:
"""
Orchestrates the creation of ``echo_range``, ``ping_time``, and ``Sv`` arrays
where the MVBS is known.
Parameters
----------
final_num_ping_bins: int
The total number of ping time bins
final_num_er_bins: int
The total number of echo range bins
ping_range: list
A list whose first element is the lowest and second element is
the highest possible number of ping time values in a given bin
er_range: list
A list whose first element is the lowest and second element is
the highest possible number of echo range values in a given bin
create_dask: bool
If True the ``Sv`` and ``echo_range`` values produced will be
dask arrays, else they will be numpy arrays.
rng: np.random.Generator
generator for random integers
Returns
-------
final_MVBS: np.ndarray
The final 2D known MVBS array
final_sv: np.ndarray
The final 2D Sv array
ping_bins: Iterable
A list whose elements are the lower and upper ping time bin ranges
er_bins: Iterable
A list whose elements are the lower and upper echo range bin ranges
final_er: np.ndarray
The final 2D ``echo_range`` array
final_ping_time: np.ndarray
The final 1D ``ping_time`` array
"""
# randomly generate the number of pings in each ping bin
num_pings_in_bin = rng.integers(low=ping_range[0], high=ping_range[1], size=final_num_ping_bins)
# create bins for ping_time dimension
ping_csum = np.cumsum(num_pings_in_bin)
ping_bins = create_bins(ping_csum)
# create bins for echo_range dimension
num_er_in_bin = rng.integers(low=er_range[0], high=er_range[1], size=final_num_er_bins)
er_csum = np.cumsum(num_er_in_bin)
er_bins = create_bins(er_csum)
# randomly select one ping bin to fill with NaNs
ping_bin_nan_ind = rng.choice(len(ping_bins))
# create the echo_range data and associated bin information
all_er_bin_nums, ping_times_in_bin, final_er_arrays = create_echo_range_related_data(ping_bins, num_pings_in_bin,
er_range, er_bins,
final_num_er_bins,
create_dask,
rng,
ping_bin_nan_ind)
# create the final echo_range array using created data and padding
final_er, max_num_er_elem = construct_2d_echo_range_array(final_er_arrays, ping_csum, create_dask)
# get final ping_time dimension
final_ping_time = np.concatenate(ping_times_in_bin).astype('datetime64[ns]')
# create the final sv array
final_sv, final_MVBS = construct_2d_sv_array(max_num_er_elem, ping_csum,
all_er_bin_nums, num_pings_in_bin,
create_dask, ping_bin_nan_ind)
return final_MVBS, final_sv, ping_bins, er_bins, final_er, final_ping_time
@pytest.fixture(
params=[
{
"create_dask": True,
"final_num_ping_bins": 10,
"final_num_er_bins": 10,
"ping_range": [10, 1000],
"er_range": [10, 1000]
},
{
"create_dask": False,
"final_num_ping_bins": 10,
"final_num_er_bins": 10,
"ping_range": [10, 1000],
"er_range": [10, 1000]
},
],
ids=[
"delayed_data",
"in_memory_data"
],
)
def bin_and_mean_2d_params(request):
"""
Obtains all necessary parameters for ``test_bin_and_mean_2d``.
"""
return list(request.param.values())
def test_bin_and_mean_2d(bin_and_mean_2d_params) -> None:
"""
Tests the function ``bin_and_mean_2d``, which is the core
method for ``compute_MVBS``. This is done by creating mock
data (which can have varying number of ``echo_range`` values
for each ``ping_time``) with known means.
Parameters
----------
create_dask: bool
If True the ``Sv`` and ``echo_range`` values produced will be
dask arrays, else they will be numpy arrays.
final_num_ping_bins: int
The total number of ping time bins
final_num_er_bins: int
The total number of echo range bins
ping_range: list
A list whose first element is the lowest and second element is
the highest possible number of ping time values in a given bin
er_range: list
A list whose first element is the lowest and second element is
the highest possible number of echo range values in a given bin
"""
# get all parameters needed to create the mock data
create_dask, final_num_ping_bins, final_num_er_bins, ping_range, er_range = bin_and_mean_2d_params
# randomly generate a seed
seed = np.random.randint(low=10, high=100000, size=1)[0]
print(f"seed used to generate mock data: {seed}")
# establish generator for random integers
rng = default_rng(seed=seed)
# seed dask random generator
if create_dask:
dask.array.random.seed(seed=seed)
# create echo_range, ping_time, and Sv arrays where the MVBS is known
known_MVBS, final_sv, ping_bins, er_bins, final_er, final_ping_time = create_known_mean_data(final_num_ping_bins,
final_num_er_bins,
ping_range, er_range,
create_dask,
rng)
# put the created ping bins into a form that works with bin_and_mean_2d
digitize_ping_bin = np.array([*ping_bins[0]] + [bin_val[1] for bin_val in ping_bins[1:-1]])
digitize_ping_bin = digitize_ping_bin.astype('datetime64[ns]')
# put the created echo range bins into a form that works with bin_and_mean_2d
digitize_er_bin = np.array([*er_bins[0]] + [bin_val[1] for bin_val in er_bins[1:]])
# calculate MVBS for mock data set
calc_MVBS = bin_and_mean_2d(arr=final_sv, bins_time=digitize_ping_bin,
bins_er=digitize_er_bin, times=final_ping_time,
echo_range=final_er, comprehensive_er_check=True)
# compare known MVBS solution against its calculated counterpart
assert np.allclose(calc_MVBS, known_MVBS, atol=1e-10, rtol=1e-10, equal_nan=True)
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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,785 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_cal_params_integration.py | import pytest
import numpy as np
import xarray as xr
import echopype as ep
@pytest.fixture
def azfp_path(test_path):
return test_path['AZFP']
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
@pytest.fixture
def ek80_cal_path(test_path):
return test_path['EK80_CAL']
def test_cal_params_intake_AZFP(azfp_path):
"""
Test cal param intake for AZFP calibration.
"""
azfp_01a_path = str(azfp_path.joinpath('17082117.01A'))
azfp_xml_path = str(azfp_path.joinpath('17041823.XML'))
ed = ep.open_raw(azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path)
# Assemble external cal param and env params
chan = ed["Sonar/Beam_group1"]["channel"]
EL_ext = xr.DataArray([100, 200, 300, 400], dims=["channel"], coords={"channel": chan}, name="EL")
env_ext = {"salinity": 30, "pressure": 10} # salinity and pressure required for AZFP cal
# Manually go through cal params intake
cal_params_manual = ep.calibrate.cal_params.get_cal_params_AZFP(
beam=ed["Sonar/Beam_group1"], vend=ed["Vendor_specific"], user_dict={"EL": EL_ext}
)
# Manually add cal params in Vendor group and construct cal object
cal_obj = ep.calibrate.calibrate_azfp.CalibrateAZFP(
echodata=ed, cal_params={"EL": EL_ext}, env_params=env_ext
)
# Check cal params ingested from both ways
assert cal_obj.cal_params["EL"].identical(cal_params_manual["EL"])
# Check against the final cal params in the calibration output
ds_Sv = ep.calibrate.compute_Sv(ed, cal_params={"EL": EL_ext}, env_params=env_ext)
assert ds_Sv["EL"].identical(cal_params_manual["EL"])
def test_cal_params_intake_EK60(ek60_path):
"""
Test cal param intake for EK60 calibration.
"""
ed = ep.open_raw(ek60_path / "ncei-wcsd" / "Summer2017-D20170620-T011027.raw", sonar_model="EK60")
# Assemble external cal param
chan = ed["Sonar/Beam_group1"]["channel"]
gain_ext = xr.DataArray([100, 200, 300], dims=["channel"], coords={"channel": chan}, name="gain_correction")
# Manually go through cal params intake
cal_params_manual = ep.calibrate.cal_params.get_cal_params_EK(
waveform_mode="CW",
freq_center=ed["Sonar/Beam_group1"]["frequency_nominal"],
beam=ed["Sonar/Beam_group1"],
vend=ed["Vendor_specific"],
user_dict={"gain_correction": gain_ext},
sonar_type="EK60",
)
# Manually add cal params in Vendor group and construct cal object
ed["Vendor_specific"]["gain_correction"].data[0, 1] = gain_ext.data[0] # GPT 18 kHz 009072058c8d 1-1 ES18-11
ed["Vendor_specific"]["gain_correction"].data[1, 2] = gain_ext.data[1] # GPT 38 kHz 009072058146 2-1 ES38B
ed["Vendor_specific"]["gain_correction"].data[2, 4] = gain_ext.data[2] # GPT 120 kHz 00907205a6d0 4-1 ES120-7C
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK60(echodata=ed, env_params=None, cal_params=None, ecs_file=None)
# Check cal params ingested from both ways
# Need to drop ping_time for cal_obj.cal_params since get_vend_cal_params_power
# retrieves sa_correction or gain_correction based on transmit_duration_nominal,
# which can vary cross ping_time
assert cal_obj.cal_params["gain_correction"].isel(ping_time=0).drop("ping_time").identical(cal_params_manual["gain_correction"])
# Check against the final cal params in the calibration output
ds_Sv = ep.calibrate.compute_Sv(ed, cal_params={"gain_correction": gain_ext})
assert ds_Sv["gain_correction"].identical(cal_params_manual["gain_correction"])
def test_cal_params_intake_EK80_BB_complex(ek80_cal_path):
"""
Test frequency-dependent cal param intake for EK80 BB complex calibration.
"""
ed = ep.open_raw(ek80_cal_path / "2018115-D20181213-T094600.raw", sonar_model="EK80")
# BB channels
chan_sel = ["WBT 714590-15 ES70-7C", "WBT 714596-15 ES38-7"]
# Assemble external freq-dependent cal param
len_cal_frequency = ed["Vendor_specific"]["cal_frequency"].size
gain_freq_dep = xr.DataArray(
np.array([np.arange(len_cal_frequency), (np.arange(len_cal_frequency) + 1000)[::-1]]),
dims=["cal_channel_id", "cal_frequency"],
coords={
"cal_channel_id": chan_sel,
"cal_frequency": ed["Vendor_specific"]["cal_frequency"],
},
)
# Manually go through cal params intake
beam = ed["Sonar/Beam_group1"].sel(channel=chan_sel)
vend = ed["Vendor_specific"].sel(channel=chan_sel)
freq_center = (
(beam["transmit_frequency_start"] + beam["transmit_frequency_stop"]).sel(channel=chan_sel) / 2)
cal_params_manual = ep.calibrate.cal_params.get_cal_params_EK(
"BB", freq_center, beam, vend, {"gain_correction": gain_freq_dep}
)
# Manually add freq-dependent cal params in Vendor group
# and construct cal object
ed["Vendor_specific"]["gain"].data[1, :] = gain_freq_dep[0, :] # WBT 714590-15 ES70-7C
ed["Vendor_specific"]["gain"].data[2, :] = gain_freq_dep[1, :] # WBT 714596-15 ES38-7
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode="BB", encode_mode="complex", cal_params=None, env_params=None
)
# Check cal params ingested from both ways
assert cal_obj.cal_params["gain_correction"].identical(cal_params_manual["gain_correction"])
# Check against the final cal params in the calibration output
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="BB", encode_mode="complex", cal_params={"gain_correction": gain_freq_dep}
)
cal_params_manual["gain_correction"].name = "gain_correction"
assert ds_Sv["gain_correction"].identical(cal_params_manual["gain_correction"])
def test_cal_params_intake_EK80_CW_complex(ek80_cal_path):
"""
Test frequency-dependent cal param intake for EK80 CW complex calibration.
"""
ed = ep.open_raw(ek80_cal_path / "2018115-D20181213-T094600.raw", sonar_model="EK80")
# CW channels
chan_sel = ["WBT 714581-15 ES18", "WBT 714583-15 ES120-7C",
"WBT 714597-15 ES333-7C", "WBT 714605-15 ES200-7C"]
# Assemble external freq-dependent cal param
len_cal_frequency = ed["Vendor_specific"]["cal_frequency"].size
gain_freq_dep = xr.DataArray(
np.array([
np.arange(len_cal_frequency),
(np.arange(len_cal_frequency) + 1000)[::-1],
(np.arange(len_cal_frequency) + 2000)[::-1],
(np.arange(len_cal_frequency) + 3000)[::-1],
]),
dims=["cal_channel_id", "cal_frequency"],
coords={
"cal_channel_id": chan_sel,
"cal_frequency": ed["Vendor_specific"]["cal_frequency"],
},
)
# Manually go through cal params intake
beam = ed["Sonar/Beam_group1"].sel(channel=chan_sel)
vend = ed["Vendor_specific"].sel(channel=chan_sel)
freq_center = beam["frequency_nominal"].sel(channel=chan_sel)
cal_params_manual = ep.calibrate.cal_params.get_cal_params_EK(
"CW", freq_center, beam, vend, {"gain_correction": gain_freq_dep}
)
cal_params_manual["gain_correction"].name = "gain_correction"
# Manually add cal params in Vendor group construct cal object
ed["Vendor_specific"]["gain_correction"].data[0, 1] = cal_params_manual["gain_correction"].data[0] # WBT 714581-15 ES18
ed["Vendor_specific"]["gain_correction"].data[1, 4] = cal_params_manual["gain_correction"].data[1] # WBT 714583-15 ES120-7C
ed["Vendor_specific"]["gain_correction"].data[4, 4] = cal_params_manual["gain_correction"].data[2] # WBT 714597-15 ES333-7C
ed["Vendor_specific"]["gain_correction"].data[5, 4] = cal_params_manual["gain_correction"].data[3] # WBT 714605-15 ES200-7C
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode="CW", encode_mode="complex", cal_params=None, env_params=None
)
# Check cal params ingested from both ways
# Need to drop ping_time for cal_obj.cal_params since get_vend_cal_params_power
# retrieves sa_correction or gain_correction based on transmit_duration_nominal,
# which can vary cross ping_time
assert cal_obj.cal_params["gain_correction"].isel(ping_time=0).drop("ping_time").identical(cal_params_manual["gain_correction"])
# Check against the final cal params in the calibration output
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="CW", encode_mode="complex", cal_params={"gain_correction": gain_freq_dep}
)
assert ds_Sv["gain_correction"].identical(cal_params_manual["gain_correction"])
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,786 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/test_core.py | from typing import TYPE_CHECKING
import tempfile
from pathlib import Path
import pytest
if TYPE_CHECKING:
from echopype.core import SonarModelsHint
from echopype.core import SONAR_MODELS
import echopype.core
@pytest.mark.parametrize(
["sonar_model", "ext"],
[
("AZFP", ".01A"),
("AZFP", ".01a"),
("AZFP", ".05C"),
("AZFP", ".12q"),
("EK60", ".raw"),
("EK60", ".RAW"),
("EK80", ".raw"),
("EK80", ".RAW"),
("EA640", ".raw"),
("EA640", ".RAW"),
("AD2CP", ".ad2cp"),
("AD2CP", ".AD2CP"),
],
)
def test_file_extension_validation(sonar_model: "SonarModelsHint", ext: str):
SONAR_MODELS[sonar_model]["validate_ext"](ext)
@pytest.mark.parametrize(
["sonar_model", "ext"],
[
("AZFP", ".001A"),
("AZFP", ".01AA"),
("AZFP", ".01aa"),
("AZFP", ".05AA"),
("AZFP", ".07!"),
("AZFP", ".01!"),
("AZFP", ".0!A"),
("AZFP", ".012"),
("AZFP", ".0AA"),
("AZFP", ".AAA"),
("AZFP", "01A"),
("EK60", "raw"),
("EK60", ".foo"),
("EK80", "raw"),
("EK80", ".foo"),
("EA640", "raw"),
("EA640", ".foo"),
("AD2CP", "ad2cp"),
("AD2CP", ".foo"),
],
)
def test_file_extension_validation_should_fail(
sonar_model: "SonarModelsHint", ext: str
):
try:
SONAR_MODELS[sonar_model]["validate_ext"](ext)
except ValueError:
pass
else:
raise ValueError(
f"\"{ext}\" should have been rejected for sonar model {sonar_model}"
)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], 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73,787 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/calibrate_azfp.py | import numpy as np
from ..echodata import EchoData
from .cal_params import get_cal_params_AZFP
from .calibrate_ek import CalibrateBase
from .env_params import get_env_params_AZFP
from .range import compute_range_AZFP
class CalibrateAZFP(CalibrateBase):
def __init__(
self, echodata: EchoData, env_params=None, cal_params=None, ecs_file=None, **kwargs
):
super().__init__(echodata, env_params, cal_params, ecs_file)
# Set sonar_type
self.sonar_type = "AZFP"
# Screen for ECS file: currently not support
if self.ecs_file is not None:
raise ValueError("Using ECS file for calibration is not currently supported for AZFP!")
# load env and cal parameters
self.env_params = get_env_params_AZFP(echodata=self.echodata, user_dict=self.env_params)
self.cal_params = get_cal_params_AZFP(
beam=self.echodata["Sonar/Beam_group1"],
vend=self.echodata["Vendor_specific"],
user_dict=self.cal_params,
)
# self.range_meter computed under self._cal_power_samples()
# because the implementation is different for Sv and TS
def compute_echo_range(self, cal_type):
"""Calculate range (``echo_range``) in meter using AZFP formula.
Note the range calculation differs for Sv and TS per AZFP matlab code.
Parameters
----------
cal_type : str
'Sv' for calculating volume backscattering strength, or
'TS' for calculating target strength
"""
self.range_meter = compute_range_AZFP(
echodata=self.echodata, env_params=self.env_params, cal_type=cal_type
)
def _cal_power_samples(self, cal_type, **kwargs):
"""Calibrate to get volume backscattering strength (Sv) from AZFP power data.
The calibration formulae used here is based on Appendix G in
the GU-100-AZFP-01-R50 Operator's Manual.
Note a Sv_offset factor that varies depending on frequency is used
in the calibration as documented on p.90.
See calc_Sv_offset() in convert/azfp.py
"""
# Compute range in meters
# range computation different for Sv and TS per AZFP matlab code
self.compute_echo_range(cal_type=cal_type)
# Compute derived params
# TODO: take care of dividing by zero encountered in log10
spreading_loss = 20 * np.log10(self.range_meter)
absorption_loss = 2 * self.env_params["sound_absorption"] * self.range_meter
SL = self.cal_params["TVR"] + 20 * np.log10(self.cal_params["VTX"]) # eq.(2)
# scaling factor (slope) in Fig.G-1, units Volts/dB], see p.84
a = self.cal_params["DS"]
EL = (
self.cal_params["EL"]
- 2.5 / a
+ self.echodata["Sonar/Beam_group1"]["backscatter_r"] / (26214 * a)
) # eq.(5)
if cal_type == "Sv":
# eq.(9)
out = (
EL
- SL
+ spreading_loss
+ absorption_loss
- 10
* np.log10(
0.5
* self.env_params["sound_speed"]
* self.echodata["Sonar/Beam_group1"]["transmit_duration_nominal"]
* self.cal_params["equivalent_beam_angle"]
)
+ self.cal_params["Sv_offset"]
) # see p.90-91 for this correction to Sv
out.name = "Sv"
elif cal_type == "TS":
# eq.(10)
out = EL - SL + 2 * spreading_loss + absorption_loss
out.name = "TS"
else:
raise ValueError("cal_type not recognized!")
# Attach calculated range (with units meter) into data set
out = out.to_dataset()
out = out.merge(self.range_meter)
# Add frequency_nominal to data set
out["frequency_nominal"] = self.echodata["Sonar/Beam_group1"]["frequency_nominal"]
# Add env and cal parameters
out = self._add_params_to_output(out)
return out
def compute_Sv(self, **kwargs):
return self._cal_power_samples(cal_type="Sv")
def compute_TS(self, **kwargs):
return self._cal_power_samples(cal_type="TS")
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"/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,788 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/widgets/utils.py | import uuid
from hashlib import md5
from datatree import DataTree
from datatree.render import RenderTree
from ..convention.utils import _get_sonar_groups
SONAR_GROUPS = _get_sonar_groups()
def html_repr(value) -> str:
return value._repr_html_()
def hash_value(value: str) -> str:
byte_string = value.encode("utf-8")
hashed = md5(byte_string)
return hashed.hexdigest()
def make_key(value: str) -> str:
return value + str(uuid.uuid4())
def _single_node_repr(node: DataTree) -> str:
"""
Obtains the string repr for a single node in a
``RenderTree`` or ``DataTree``.
Parameters
----------
node: DataTree
A single node obtained from a ``RenderTree`` or ``DataTree``
Returns
-------
node_info: str
string representation of repr for the input ``node``
"""
# initialize node_pathstr
node_pathstr = "Top-level"
# obtain the appropriate group name and get its descriptions from the yaml
if node.name != "root":
node_pathstr = node.path[1:]
sonar_group = SONAR_GROUPS[node_pathstr]
if "Beam_group" in sonar_group["name"]:
# get description of Beam_group directly from the Sonar group
group_descr = str(
node.parent["/Sonar"].ds.beam_group_descr.sel(beam_group=sonar_group["name"]).values
)
else:
# get description of group from yaml file
group_descr = sonar_group["description"]
# construct the final node information string for repr
node_info = f"{sonar_group['name']}: {group_descr}"
return node_info
def tree_repr(tree: DataTree) -> str:
renderer = RenderTree(tree)
lines = []
for pre, _, node in renderer:
if node.has_data or node.has_attrs:
node_repr = _single_node_repr(node)
node_line = f"{pre}{node_repr.splitlines()[0]}"
lines.append(node_line)
return "\n".join(lines)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,789 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/simrad.py | """
Contains functions that are specific to Simrad echo sounders
"""
from typing import Optional, Tuple
import numpy as np
from .echodata import EchoData
def check_input_args_combination(
waveform_mode: str, encode_mode: str, pulse_compression: bool = None
) -> None:
"""
Checks that the ``waveform_mode`` and ``encode_mode`` have
the correct values and that the combination of input arguments are valid, without
considering the actual data.
Parameters
----------
waveform_mode: str
Type of transmit waveform
encode_mode: str
Type of encoded return echo data
pulse_compression: bool
States whether pulse compression should be used
"""
if waveform_mode not in ["CW", "BB"]:
raise ValueError("The input waveform_mode must be either 'CW' or 'BB'!")
if encode_mode not in ["complex", "power"]:
raise ValueError("The input encode_mode must be either 'complex' or 'power'!")
# BB has complex data only, but CW can have complex or power data
if (waveform_mode == "BB") and (encode_mode == "power"):
raise ValueError(
"Data from broadband ('BB') transmission must be recorded as complex samples"
)
# make sure that we have BB and complex inputs, if pulse compression is selected
if pulse_compression is not None:
if pulse_compression and ((waveform_mode != "BB") or (encode_mode != "complex")):
raise RuntimeError(
"Pulse compression can only be used with "
"waveform_mode='BB' and encode_mode='complex'"
)
def _retrieve_correct_beam_group_EK60(
echodata: EchoData, waveform_mode: str, encode_mode: str
) -> Optional[str]:
"""
Ensures that the provided ``waveform_mode`` and ``encode_mode`` are consistent
with the EK60-like data supplied by ``echodata``. Additionally, select the
appropriate beam group corresponding to this input.
Parameters
----------
echodata: EchoData
An ``EchoData`` object holding the data
waveform_mode : {"CW", "BB"}
Type of transmit waveform
encode_mode : {"complex", "power"}
Type of encoded return echo data
Returns
-------
power_ed_group: str, optional
The ``EchoData`` beam group path containing the power data
"""
# initialize power EchoData group value
power_ed_group = None
# EK60-like sensors must have 'power' and 'CW' modes only
if waveform_mode != "CW":
raise RuntimeError("Incorrect waveform_mode input provided!")
if encode_mode != "power":
raise RuntimeError("Incorrect encode_mode input provided!")
# ensure that no complex data exists (this should never be triggered)
if "backscatter_i" in echodata["Sonar/Beam_group1"].variables:
raise RuntimeError(
"Provided echodata object does not correspond to an EK60-like "
"sensor, but is labeled as data from an EK60-like sensor!"
)
else:
power_ed_group = "Sonar/Beam_group1"
return power_ed_group
def _retrieve_correct_beam_group_EK80(
echodata: EchoData, waveform_mode: str, encode_mode: str
) -> Tuple[Optional[str], Optional[str]]:
"""
Ensures that the provided ``waveform_mode`` and ``encode_mode`` are consistent
with the EK80-like data supplied by ``echodata``. Additionally, select the
appropriate beam group corresponding to this input.
Parameters
----------
echodata: EchoData
An ``EchoData`` object holding the data
waveform_mode : {"CW", "BB"}
Type of transmit waveform
encode_mode : {"complex", "power"}
Type of encoded return echo data
Returns
-------
power_ed_group: str, optional
The ``EchoData`` beam group path containing the power data
complex_ed_group: str, optional
The ``EchoData`` beam group path containing the complex data
"""
# initialize power and complex EchoData group values
power_ed_group = None
complex_ed_group = None
transmit_type = echodata["Sonar/Beam_group1"]["transmit_type"]
# assume transmit_type identical for all pings in a channel
# TODO: change when allowing within-channel CW-BB switch
first_ping_transmit_type = transmit_type.isel(ping_time=0)
if waveform_mode == "BB":
# check BB waveform_mode, BB must always have complex data, can have 2 beam groups
# when echodata contains CW power and BB complex samples
if np.all(first_ping_transmit_type == "CW"):
raise ValueError("waveform_mode='BB', but complex data does not exist!")
elif echodata["Sonar/Beam_group2"] is not None:
power_ed_group = "Sonar/Beam_group2"
complex_ed_group = "Sonar/Beam_group1"
else:
complex_ed_group = "Sonar/Beam_group1"
else:
# CW can have complex or power data, so we just need to make sure that
# 1) complex samples always exist in Sonar/Beam_group1
# 2) power samples are in Sonar/Beam_group1 if only one beam group exists
# 3) power samples are in Sonar/Beam_group2 if two beam groups exist
# Raise error if waveform_mode="CW" but CW data does not exist (not a single ping is CW)
# TODO: change when allowing within-channel CW-BB switch
if encode_mode == "complex" and np.all(first_ping_transmit_type != "CW"):
raise ValueError("waveform_mode='CW', but all data are broadband (BB)!")
if echodata["Sonar/Beam_group2"] is None:
if encode_mode == "power":
# power samples must be in Sonar/Beam_group1 (thus no complex samples)
if "backscatter_i" in echodata["Sonar/Beam_group1"].variables:
raise RuntimeError("Data provided does not correspond to encode_mode='power'!")
else:
power_ed_group = "Sonar/Beam_group1"
elif encode_mode == "complex":
# complex samples must be in Sonar/Beam_group1
if "backscatter_i" not in echodata["Sonar/Beam_group1"].variables:
raise RuntimeError(
"Data provided does not correspond to encode_mode='complex'!"
)
else:
complex_ed_group = "Sonar/Beam_group1"
else:
# complex should be in Sonar/Beam_group1 and power in Sonar/Beam_group2
# the RuntimeErrors below should never be triggered
if "backscatter_i" not in echodata["Sonar/Beam_group1"].variables:
raise RuntimeError(
"Complex data does not exist in Sonar/Beam_group1, "
"input echodata object must have been incorrectly constructed!"
)
elif "backscatter_r" not in echodata["Sonar/Beam_group2"].variables:
raise RuntimeError(
"Power data does not exist in Sonar/Beam_group2, "
"input echodata object must have been incorrectly constructed!"
)
else:
complex_ed_group = "Sonar/Beam_group1"
power_ed_group = "Sonar/Beam_group2"
return power_ed_group, complex_ed_group
def retrieve_correct_beam_group(echodata: EchoData, waveform_mode: str, encode_mode: str) -> str:
"""
A function to make sure that the user has provided the correct
``waveform_mode`` and ``encode_mode`` inputs based off of the
supplied ``echodata`` object. Additionally, determine the
``EchoData`` beam group corresponding to ``encode_mode``.
Parameters
----------
echodata: EchoData
An ``EchoData`` object holding the data corresponding to the
waveform and encode modes
waveform_mode : {"CW", "BB"}
Type of transmit waveform
encode_mode : {"complex", "power"}
Type of encoded return echo data
pulse_compression: bool
States whether pulse compression should be used
Returns
-------
str
The ``EchoData`` beam group path corresponding to the ``encode_mode`` input
"""
if echodata.sonar_model in ["EK60", "ES70"]:
# initialize complex_data_location (needed only for EK60)
complex_ed_group = None
# check modes against data for EK60 and get power EchoData group
power_ed_group = _retrieve_correct_beam_group_EK60(echodata, waveform_mode, encode_mode)
elif echodata.sonar_model in ["EK80", "ES80", "EA640"]:
# check modes against data for EK80 and get power/complex EchoData groups
power_ed_group, complex_ed_group = _retrieve_correct_beam_group_EK80(
echodata, waveform_mode, encode_mode
)
else:
# raise error for unknown or unaccounted for sonar model
raise RuntimeError("EchoData was produced by a non-Simrad or unknown Simrad echo sounder!")
if encode_mode == "complex":
return complex_ed_group
else:
return power_ed_group
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", 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"/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,790 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/convert/test_convert_ek60.py | import numpy as np
import pandas as pd
from scipy.io import loadmat
from echopype import open_raw
import pytest
@pytest.fixture
def ek60_path(test_path):
return test_path["EK60"]
# raw_paths = ['./echopype/test_data/ek60/set1/' + file
# for file in os.listdir('./echopype/test_data/ek60/set1')] # 2 range lengths
# raw_path = ['./echopype/test_data/ek60/set2/' + file
# for file in os.listdir('./echopype/test_data/ek60/set2')] # 3 range lengths
# Other data files
# raw_filename = 'data_zplsc/OceanStarr_2017-D20170725-T004612.raw' # OceanStarr 2 channel EK60
# raw_filename = '../data/DY1801_EK60-D20180211-T164025.raw' # Dyson 5 channel EK60
# raw_filename = 'data_zplsc/D20180206-T000625.raw # EK80
def test_convert_ek60_matlab_raw(ek60_path):
"""Compare parsed Beam group data with Matlab outputs."""
ek60_raw_path = str(
ek60_path.joinpath('DY1801_EK60-D20180211-T164025.raw')
)
ek60_matlab_path = str(
ek60_path.joinpath(
'from_matlab', 'DY1801_EK60-D20180211-T164025_rawData.mat'
)
)
# Convert file
echodata = open_raw(raw_file=ek60_raw_path, sonar_model='EK60')
# Compare with matlab outputs
ds_matlab = loadmat(ek60_matlab_path)
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
# check water_level
assert np.allclose(echodata["Platform"]["water_level"], 9.14999962, rtol=0)
# power
assert np.allclose(
[
ds_matlab['rawData'][0]['pings'][0]['power'][0][fidx]
for fidx in range(5)
],
echodata["Sonar/Beam_group1"].backscatter_r.transpose(
'channel', 'range_sample', 'ping_time'
),
rtol=0,
atol=1.6e-5,
)
# angle: alongship and athwartship
for angle in ['alongship', 'athwartship']:
assert np.array_equal(
[
ds_matlab['rawData'][0]['pings'][0][angle][0][fidx]
for fidx in range(5)
],
echodata["Sonar/Beam_group1"]['angle_' + angle].transpose(
'channel', 'range_sample', 'ping_time'
),
)
def test_convert_ek60_echoview_raw(ek60_path):
"""Compare parsed power data (count) with csv exported by EchoView."""
ek60_raw_path = str(
ek60_path.joinpath('DY1801_EK60-D20180211-T164025.raw')
)
ek60_csv_path = [
ek60_path.joinpath(
'from_echoview', 'DY1801_EK60-D20180211-T164025-Power%d.csv' % freq
)
for freq in [18, 38, 70, 120, 200]
]
# Read csv files exported by EchoView
channels = []
for file in ek60_csv_path:
channels.append(
pd.read_csv(file, header=None, skiprows=[0]).iloc[:, 13:]
)
test_power = np.stack(channels)
# Convert to netCDF and check
echodata = open_raw(raw_file=ek60_raw_path, sonar_model='EK60')
# get indices of sorted frequency_nominal values. This is necessary
# because the frequency_nominal values are not always in ascending order.
sorted_freq_ind = np.argsort(echodata["Sonar/Beam_group1"].frequency_nominal)
for fidx, atol in zip(range(5), [1e-5, 1.1e-5, 1.1e-5, 1e-5, 1e-5]):
assert np.allclose(
test_power[fidx, :, :],
echodata["Sonar/Beam_group1"].backscatter_r.isel(
channel=sorted_freq_ind[fidx],
ping_time=slice(None, 10),
range_sample=slice(1, None)
),
atol=9e-6,
rtol=atol,
)
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
# check water_level
assert np.allclose(echodata["Platform"]["water_level"], 9.14999962, rtol=0)
def test_convert_ek60_duplicate_frequencies(ek60_path):
"""Convert a file with duplicate frequencies"""
raw_path = (
ek60_path
/ "DY1002_EK60-D20100318-T023008_rep_freq.raw"
)
ed = open_raw(raw_path, "EK60")
truth_chan_vals = np.array(['GPT 18 kHz 009072034d45 1-1 ES18-11',
'GPT 38 kHz 009072033fa2 2-1 ES38B',
'GPT 70 kHz 009072058c6c 3-1 ES70-7C',
'GPT 70 kHz 009072058c6c 3-2 ES70-7C',
'GPT 120 kHz 00907205794e 4-1 ES120-7C',
'GPT 200 kHz 0090720346a8 5-1 ES200-7C'], dtype='<U37')
truth_freq_nom_vals = np.array([18000., 38000., 70000.,
70000., 120000., 200000.], dtype=np.float64)
assert np.allclose(ed['Sonar/Beam_group1'].frequency_nominal,
truth_freq_nom_vals, rtol=1e-05, atol=1e-08)
assert np.all(ed['Sonar/Beam_group1'].channel.values == truth_chan_vals)
def test_convert_ek60_splitbeam_no_angle(ek60_path):
"""Convert a file from a split-beam setup that does not record angle data."""
raw_path = (
ek60_path
/ "NBP_B050N-D20180118-T090228.raw"
)
ed = open_raw(raw_path, "EK60")
assert "angle_athwartship" not in ed["Sonar/Beam_group1"]
assert "angle_alongship" not in ed["Sonar/Beam_group1"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,791 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parsed_to_zarr_ek60.py | import numpy as np
import pandas as pd
import psutil
from .parsed_to_zarr import Parsed2Zarr
class Parsed2ZarrEK60(Parsed2Zarr):
"""
Facilitates the writing of parsed data to
a zarr file for the EK60 sensor.
"""
def __init__(self, parser_obj):
super().__init__(parser_obj)
self.power_dims = ["timestamp", "channel"]
self.angle_dims = ["timestamp", "channel"]
self.p2z_ch_ids = {} # channel ids for power, angle, complex
self.datagram_df = None # df created from zarr variables
# get the channel sort rule for EK60 type sensors
if "transceivers" in self.parser_obj.config_datagram:
# get channel and channel_id association and sort by channel_id
channels_old = list(self.parser_obj.config_datagram["transceivers"].keys())
# sort the channels in ascending order
channels_new = channels_old[:]
channels_new.sort(reverse=False)
# obtain sort rule for the channel index
self.channel_sort_rule = {str(ch): channels_new.index(ch) for ch in channels_old}
@staticmethod
def _get_string_dtype(pd_series: pd.Index) -> str:
"""
Returns the string dtype in a format that
works for zarr.
Parameters
----------
pd_series: pd.Index
A series where all of the elements are strings
"""
if all(pd_series.map(type) == str):
max_len = pd_series.map(len).max()
dtype = f"<U{max_len}"
else:
raise ValueError("All elements of pd_series must be strings!")
return dtype
def _write_power(self, df: pd.DataFrame, max_mb: int) -> None:
"""
Writes the power data and associated indices
to a zarr group.
Parameters
----------
df : pd.DataFrame
DataFrame that contains power data
max_mb : int
Maximum MB allowed for each chunk
"""
# obtain power data
power_series = df.set_index(self.power_dims)["power"].copy()
# get unique indices
times = power_series.index.get_level_values(0).unique()
channels = power_series.index.get_level_values(1).unique()
# sort the channels based on rule
_, indexer = channels.map(self.channel_sort_rule).sort_values(
ascending=True, return_indexer=True
)
channels = channels[indexer]
self.p2z_ch_ids["power"] = channels.values # store channel ids for variable
# create multi index using the product of the unique dims
unique_dims = [times, channels]
power_series = self.set_multi_index(power_series, unique_dims)
# write power data to the power group
zarr_grp = self.zarr_root.create_group("power")
self.write_df_column(
pd_series=power_series,
zarr_grp=zarr_grp,
is_array=True,
unique_time_ind=times,
max_mb=max_mb,
)
# write the unique indices to the power group
zarr_grp.array(
name=self.power_dims[0], data=times.values, dtype=times.dtype.str, fill_value="NaT"
)
dtype = self._get_string_dtype(channels)
zarr_grp.array(name=self.power_dims[1], data=channels.values, dtype=dtype, fill_value=None)
@staticmethod
def _split_angle_data(angle_series: pd.Series) -> pd.DataFrame:
"""
Splits the 2D angle data into two 1D arrays
representing angle_athwartship and angle_alongship,
for each element in ``angle_series``.
Parameters
----------
angle_series : pd.Series
Series representing the angle data
Returns
-------
DataFrame with columns angle_athwartship and
angle_alongship obtained from splitting the
2D angle data, with that same index as
``angle_series``
"""
# split each angle element into angle_athwartship and angle_alongship
angle_split = angle_series.apply(
lambda x: [x[:, 0], x[:, 1]] if isinstance(x, np.ndarray) else [None, None]
)
return pd.DataFrame(
data=angle_split.to_list(),
columns=["angle_athwartship", "angle_alongship"],
index=angle_series.index,
)
def _write_angle(self, df: pd.DataFrame, max_mb: int) -> None:
"""
Writes the angle data and associated indices
to a zarr group.
Parameters
----------
df : pd.DataFrame
DataFrame that contains angle data
max_mb : int
Maximum MB allowed for each chunk
"""
# obtain angle data
angle_series = df.set_index(self.angle_dims)["angle"].copy()
angle_df = self._split_angle_data(angle_series)
# get unique indices
times = angle_df.index.get_level_values(0).unique()
channels = angle_df.index.get_level_values(1).unique()
# sort the channels based on rule
_, indexer = channels.map(self.channel_sort_rule).sort_values(
ascending=True, return_indexer=True
)
channels = channels[indexer]
self.p2z_ch_ids["angle"] = channels.values # store channel ids for variable
# create multi index using the product of the unique dims
unique_dims = [times, channels]
angle_df = self.set_multi_index(angle_df, unique_dims)
# write angle data to the angle group
zarr_grp = self.zarr_root.create_group("angle")
for column in angle_df:
self.write_df_column(
pd_series=angle_df[column],
zarr_grp=zarr_grp,
is_array=True,
unique_time_ind=times,
max_mb=max_mb,
)
# write the unique indices to the angle group
zarr_grp.array(
name=self.angle_dims[0], data=times.values, dtype=times.dtype.str, fill_value="NaT"
)
dtype = self._get_string_dtype(channels)
zarr_grp.array(name=self.angle_dims[1], data=channels.values, dtype=dtype, fill_value=None)
def _get_power_angle_size(self, df: pd.DataFrame) -> int:
"""
Returns the total memory in bytes required to
store the expanded power and angle data.
Parameters
----------
df: pd.DataFrame
DataFrame containing the power, angle, and
the appropriate dimension data
"""
# get unique indices
times = df[self.power_dims[0]].unique()
channels = df[self.power_dims[1]].unique()
# get final form of index
multi_index = pd.MultiIndex.from_product([times, channels])
# get the total memory required for expanded zarr variables
pow_mem = self.array_series_bytes(df["power"], multi_index.shape[0])
angle_mem = self.array_series_bytes(df["angle"], multi_index.shape[0])
return pow_mem + angle_mem
def whether_write_to_zarr(self, mem_mult: float = 0.3) -> bool:
"""
Determines if the zarr data provided will expand
into a form that is larger than a percentage of
the total physical RAM.
Parameters
----------
mem_mult : float
Multiplier for total physical RAM
Notes
-----
If ``mem_mult`` times the total RAM is less
than the total memory required to store the
expanded zarr variables, this function will
return True, otherwise False.
"""
# create datagram df, if it does not exist
if not isinstance(self.datagram_df, pd.DataFrame):
self.datagram_df = pd.DataFrame.from_dict(self.parser_obj.zarr_datagrams)
total_mem = self._get_power_angle_size(self.datagram_df)
# get statistics about system memory usage
mem = psutil.virtual_memory()
zarr_dgram_size = self._get_zarr_dgrams_size()
# approx. the amount of memory that will be used after expansion
req_mem = mem.used - zarr_dgram_size + total_mem
# free memory, if we no longer need it
if mem.total * mem_mult > req_mem:
del self.datagram_df
else:
del self.parser_obj.zarr_datagrams
return mem.total * mem_mult < req_mem
def datagram_to_zarr(self, max_mb: int) -> None:
"""
Facilitates the conversion of a list of
datagrams to a form that can be written
to a zarr store.
Parameters
----------
max_mb : int
Maximum MB allowed for each chunk
Notes
-----
This function specifically writes chunks along the time
index.
The chunking routine evenly distributes the times such
that each chunk differs by at most one time. This makes
it so that the memory required for each chunk is approximately
the same.
"""
self._create_zarr_info()
# create datagram df, if it does not exist
if not isinstance(self.datagram_df, pd.DataFrame):
self.datagram_df = pd.DataFrame.from_dict(self.parser_obj.zarr_datagrams)
del self.parser_obj.zarr_datagrams # free memory
# convert channel column to a string
self.datagram_df["channel"] = self.datagram_df["channel"].astype(str)
if not self.datagram_df.empty:
self._write_power(df=self.datagram_df, max_mb=max_mb)
del self.datagram_df["power"] # free memory
if not self.datagram_df.empty:
self._write_angle(df=self.datagram_df, max_mb=max_mb)
del self.datagram_df # free memory
self._close_store()
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,792 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/mask/api.py | import datetime
import operator as op
import pathlib
from typing import List, Optional, Union
import numpy as np
import xarray as xr
from ..utils.io import validate_source_ds_da
from ..utils.prov import add_processing_level, echopype_prov_attrs, insert_input_processing_level
# lookup table with key string operator and value as corresponding Python operator
str2ops = {
">": op.gt,
"<": op.lt,
"<=": op.le,
">=": op.ge,
"==": op.eq,
}
def _validate_source_ds(source_ds, storage_options_ds):
"""
Validate the input ``source_ds`` and the associated ``storage_options_mask``.
"""
# Validate the source_ds type or path (if it is provided)
source_ds, file_type = validate_source_ds_da(source_ds, storage_options_ds)
if isinstance(source_ds, str):
# open up Dataset using source_ds path
source_ds = xr.open_dataset(source_ds, engine=file_type, chunks={}, **storage_options_ds)
# Check source_ds coordinates
if "ping_time" not in source_ds or "range_sample" not in source_ds:
raise ValueError("'source_ds' must have coordinates 'ping_time' and 'range_sample'!")
return source_ds
def _validate_and_collect_mask_input(
mask: Union[
Union[xr.DataArray, str, pathlib.Path], List[Union[xr.DataArray, str, pathlib.Path]]
],
storage_options_mask: Union[dict, List[dict]],
) -> Union[xr.DataArray, List[xr.DataArray]]:
"""
Validate that the input ``mask`` and associated ``storage_options_mask`` are correctly
provided to ``apply_mask``. Additionally, form the mask input that should be used
in the core routine of ``apply_mask``.
Parameters
----------
mask: xr.DataArray, str, pathlib.Path, or a list of these datatypes
The mask(s) to be applied. Can be a single input or list that corresponds to a
DataArray or a path. If a path is provided this should point to a zarr or netcdf
file with only one data variable in it.
storage_options_mask: dict or list of dict, default={}
Any additional parameters for the storage backend, corresponding to the
path provided for ``mask``. If ``mask`` is a list, then this input should either
be a list of dictionaries or a single dictionary with storage options that
correspond to all elements in ``mask`` that are paths.
Returns
-------
xr.DataArray or list of xr.DataArray
If the ``mask`` input is a single value, then the corresponding DataArray will be
returned, else a list of DataArrays corresponding to the input masks will be returned
Raises
------
ValueError
If ``mask`` is a single-element and ``storage_options_mask`` is not a single dict
TypeError
If ``storage_options_mask`` is not a list of dict or a dict
"""
if isinstance(mask, list):
# if storage_options_mask is not a list create a list of
# length len(mask) with elements storage_options_mask
if not isinstance(storage_options_mask, list):
if not isinstance(storage_options_mask, dict):
raise TypeError("storage_options_mask must be a list of dict or a dict!")
storage_options_mask = [storage_options_mask] * len(mask)
else:
# ensure all element of storage_options_mask are a dict
if not all([isinstance(elem, dict) for elem in storage_options_mask]):
raise TypeError("storage_options_mask must be a list of dict or a dict!")
for mask_ind in range(len(mask)):
# validate the mask type or path (if it is provided)
mask_val, file_type = validate_source_ds_da(
mask[mask_ind], storage_options_mask[mask_ind]
)
# replace mask element path with its corresponding DataArray
if isinstance(mask_val, (str, pathlib.Path)):
# open up DataArray using mask path
mask[mask_ind] = xr.open_dataarray(
mask_val, engine=file_type, chunks={}, **storage_options_mask[mask_ind]
)
# check mask coordinates
# the coordinate sequence matters, so fix the tuple form
allowed_dims = [
("ping_time", "range_sample"),
("channel", "ping_time", "range_sample"),
]
if mask[mask_ind].dims not in allowed_dims:
raise ValueError("All masks must have dimensions ('ping_time', 'range_sample')!")
else:
if not isinstance(storage_options_mask, dict):
raise ValueError(
"The provided input storage_options_mask should be a single "
"dict because mask is a single value!"
)
# validate the mask type or path (if it is provided)
mask, file_type = validate_source_ds_da(mask, storage_options_mask)
if isinstance(mask, (str, pathlib.Path)):
# open up DataArray using mask path
mask = xr.open_dataarray(mask, engine=file_type, chunks={}, **storage_options_mask)
return mask
def _check_var_name_fill_value(
source_ds: xr.Dataset, var_name: str, fill_value: Union[int, float, np.ndarray, xr.DataArray]
) -> Union[int, float, np.ndarray, xr.DataArray]:
"""
Ensures that the inputs ``var_name`` and ``fill_value`` for the function
``apply_mask`` were appropriately provided.
Parameters
----------
source_ds: xr.Dataset
A Dataset that contains the variable ``var_name``
var_name: str
The variable name in ``source_ds`` that the mask should be applied to
fill_value: int or float or np.ndarray or xr.DataArray
Specifies the value(s) at false indices
Returns
-------
fill_value: int or float or np.ndarray or xr.DataArray
fill_value with sanitized dimensions
Raises
------
TypeError
If ``var_name`` or ``fill_value`` are not an accepted type
ValueError
If the Dataset ``source_ds`` does not contain ``var_name``
ValueError
If ``fill_value`` is an array and not the same shape as ``var_name``
"""
# check the type of var_name
if not isinstance(var_name, str):
raise TypeError("The input var_name must be a string!")
# ensure var_name is in source_ds
if var_name not in source_ds.variables:
raise ValueError("The Dataset source_ds does not contain the variable var_name!")
# check the type of fill_value
if not isinstance(fill_value, (int, float, np.ndarray, xr.DataArray)):
raise TypeError(
"The input fill_value must be of type int or " "float or np.ndarray or xr.DataArray!"
)
# make sure that fill_values is the same shape as var_name
if isinstance(fill_value, (np.ndarray, xr.DataArray)):
if isinstance(fill_value, xr.DataArray):
fill_value = fill_value.data.squeeze() # squeeze out length=1 channel dimension
elif isinstance(fill_value, np.ndarray):
fill_value = fill_value.squeeze() # squeeze out length=1 channel dimension
source_ds_shape = (
source_ds[var_name].isel(channel=0).shape
if "channel" in source_ds[var_name].coords
else source_ds[var_name].shape
)
if fill_value.shape != source_ds_shape:
raise ValueError(
f"If fill_value is an array it must be of the same shape as {var_name}!"
)
return fill_value
def _variable_prov_attrs(
masked_da: xr.DataArray, source_mask: Union[xr.DataArray, List[xr.DataArray]]
) -> dict:
"""
Extract and compose masked Sv provenance attributes from the masked Sv and the
masks used to generate it.
Parameters
----------
masked_da: xr.DataArray
Masked Sv
source_mask: Union[xr.DataArray, List[xr.DataArray]]
Individual mask or list of masks used to create the masked Sv
Returns
-------
dict
Dictionary of provenance attributes (attribute name and value) for the intended variable.
"""
# Modify core variable attributes
attrs = {
"long_name": "Volume backscattering strength, masked (Sv re 1 m-1)",
"actual_range": [
round(float(masked_da.min().values), 2),
round(float(masked_da.max().values), 2),
],
}
# Add history attribute
history_attr = f"{datetime.datetime.utcnow()} +00:00. " "Created masked Sv dataarray." # noqa
attrs = {**attrs, **{"history": history_attr}}
# Add attributes from the mask DataArray, if present
# Handle only a single mask. If not passed to apply_mask as a single DataArray,
# will use the first mask of the list passed to apply_mask
# TODO: Expand it to handle attributes from multiple masks
if isinstance(source_mask, xr.DataArray) or (
isinstance(source_mask, list) and isinstance(source_mask[0], xr.DataArray)
):
use_mask = source_mask[0] if isinstance(source_mask, list) else source_mask
if len(use_mask.attrs) > 0:
mask_attrs = use_mask.attrs.copy()
if "history" in mask_attrs:
# concatenate the history string as new line
attrs["history"] += f"\n{mask_attrs['history']}"
mask_attrs.pop("history")
attrs = {**attrs, **mask_attrs}
return attrs
@add_processing_level("L3*")
def apply_mask(
source_ds: Union[xr.Dataset, str, pathlib.Path],
mask: Union[xr.DataArray, str, pathlib.Path, List[Union[xr.DataArray, str, pathlib.Path]]],
var_name: str = "Sv",
fill_value: Union[int, float, np.ndarray, xr.DataArray] = np.nan,
storage_options_ds: dict = {},
storage_options_mask: Union[dict, List[dict]] = {},
) -> xr.Dataset:
"""
Applies the provided mask(s) to the Sv variable ``var_name``
in the provided Dataset ``source_ds``.
Parameters
----------
source_ds: xr.Dataset, str, or pathlib.Path
Points to a Dataset that contains the variable the mask should be applied to
mask: xr.DataArray, str, pathlib.Path, or a list of these datatypes
The mask(s) to be applied.
Can be a single input or list that corresponds to a DataArray or a path.
Each entry in the list must have dimensions ``('ping_time', 'range_sample')``.
Multi-channel masks are not currently supported.
If a path is provided this should point to a zarr or netcdf file with only
one data variable in it.
If the input ``mask`` is a list, a logical AND will be used to produce the final
mask that will be applied to ``var_name``.
var_name: str, default="Sv"
The Sv variable name in ``source_ds`` that the mask should be applied to.
This variable needs to have coordinates ``ping_time`` and ``range_sample``,
and can optionally also have coordinate ``channel``.
In the case of a multi-channel Sv data variable, the ``mask`` will be broadcast
to all channels.
fill_value: int, float, np.ndarray, or xr.DataArray, default=np.nan
Value(s) at masked indices.
If ``fill_value`` is of type ``np.ndarray`` or ``xr.DataArray``,
it must have the same shape as each entry of ``mask``.
storage_options_ds: dict, default={}
Any additional parameters for the storage backend, corresponding to the
path provided for ``source_ds``
storage_options_mask: dict or list of dict, default={}
Any additional parameters for the storage backend, corresponding to the
path provided for ``mask``. If ``mask`` is a list, then this input should either
be a list of dictionaries or a single dictionary with storage options that
correspond to all elements in ``mask`` that are paths.
Returns
-------
xr.Dataset
A Dataset with the same format of ``source_ds`` with the mask(s) applied to ``var_name``
"""
# Validate the source_ds
source_ds = _validate_source_ds(source_ds, storage_options_ds)
# Validate and form the mask input to be used downstream
mask = _validate_and_collect_mask_input(mask, storage_options_mask)
# Check var_name and sanitize fill_value dimensions if an array
fill_value = _check_var_name_fill_value(source_ds, var_name, fill_value)
# Obtain final mask to be applied to var_name
if isinstance(mask, list):
# perform a logical AND element-wise operation across the masks
final_mask = np.logical_and.reduce(mask)
# xr.where has issues with attrs when final_mask is an array, so we make it a DataArray
final_mask = xr.DataArray(final_mask, coords=mask[0].coords)
else:
final_mask = mask
# Sanity check: final_mask should be of the same shape as source_ds[var_name]
# along the ping_time and range_sample dimensions
def get_ch_shape(da):
return da.isel(channel=0).shape if "channel" in da.dims else da.shape
# Below operate on the actual data array to be masked
source_da = source_ds[var_name]
source_da_shape = get_ch_shape(source_da)
final_mask_shape = get_ch_shape(final_mask)
if final_mask_shape != source_da_shape:
raise ValueError(
f"The final constructed mask is not of the same shape as source_ds[{var_name}] "
"along the ping_time and range_sample dimensions!"
)
# final_mask is always an xr.DataArray with at most length=1 channel dimension
if "channel" in final_mask.dims:
final_mask = final_mask.isel(channel=0)
# Make sure fill_value and final_mask are expanded in dimensions
if "channel" in source_da.dims:
if isinstance(fill_value, np.ndarray):
fill_value = np.array([fill_value] * source_da["channel"].size)
final_mask = np.array([final_mask.data] * source_da["channel"].size)
# Apply the mask to var_name
# Somehow keep_attrs=True errors out here, so will attach later
var_name_masked = xr.where(final_mask, x=source_da, y=fill_value)
# Obtain a shallow copy of source_ds
output_ds = source_ds.copy(deep=False)
# Replace var_name with var_name_masked
output_ds[var_name] = var_name_masked
output_ds[var_name] = output_ds[var_name].assign_attrs(source_da.attrs)
# Add or modify variable and global (dataset) provenance attributes
output_ds[var_name] = output_ds[var_name].assign_attrs(
_variable_prov_attrs(output_ds[var_name], mask)
)
process_type = "mask"
prov_dict = echopype_prov_attrs(process_type=process_type)
prov_dict[f"{process_type}_function"] = "mask.apply_mask"
output_ds = output_ds.assign_attrs(prov_dict)
output_ds = insert_input_processing_level(output_ds, input_ds=source_ds)
return output_ds
def _check_freq_diff_non_data_inputs(
freqAB: Optional[List[float]] = None,
chanAB: Optional[List[str]] = None,
operator: str = ">",
diff: Union[float, int] = None,
) -> None:
"""
Checks that the non-data related inputs of ``frequency_differencing`` (i.e. ``freqAB``,
``chanAB``, ``operator``, ``diff``) were correctly provided.
Parameters
----------
freqAB: list of float, optional
The pair of nominal frequencies to be used for frequency-differencing, where
the first element corresponds to ``freqA`` and the second element corresponds
to ``freqB``
chanAB: list of float, optional
The pair of channels that will be used to select the nominal frequencies to be
used for frequency-differencing, where the first element corresponds to ``freqA``
and the second element corresponds to ``freqB``
operator: {">", "<", "<=", ">=", "=="}
The operator for the frequency-differencing
diff: float or int
The threshold of Sv difference between frequencies
"""
# check that either freqAB or chanAB are provided and they are a list of length 2
if (freqAB is None) and (chanAB is None):
raise ValueError("Either freqAB or chanAB must be given!")
elif (freqAB is not None) and (chanAB is not None):
raise ValueError("Only freqAB or chanAB must be given, but not both!")
elif freqAB is not None:
if not isinstance(freqAB, list):
raise TypeError("freqAB must be a list!")
elif len(set(freqAB)) != 2:
raise ValueError("freqAB must be a list of length 2 with unique elements!")
else:
if not isinstance(chanAB, list):
raise TypeError("chanAB must be a list!")
elif len(set(chanAB)) != 2:
raise ValueError("chanAB must be a list of length 2 with unique elements!")
# check that operator is a string and a valid operator
if not isinstance(operator, str):
raise TypeError("operator must be a string!")
else:
if operator not in [">", "<", "<=", ">=", "=="]:
raise ValueError("Invalid operator!")
# ensure that diff is a float or an int
if not isinstance(diff, (float, int)):
raise TypeError("diff must be a float or int!")
def _check_source_Sv_freq_diff(
source_Sv: xr.Dataset,
freqAB: Optional[List[float]] = None,
chanAB: Optional[List[str]] = None,
) -> None:
"""
Ensures that ``source_Sv`` contains ``channel`` as a coordinate and
``frequency_nominal`` as a variable, the provided list input
(``freqAB`` or ``chanAB``) are contained in the coordinate ``channel``
or variable ``frequency_nominal``, and ``source_Sv`` does not have
repeated values for ``channel`` and ``frequency_nominal``.
Parameters
----------
source_Sv: xr.Dataset
A Dataset that contains the Sv data to create a mask for
freqAB: list of float, optional
The pair of nominal frequencies to be used for frequency-differencing, where
the first element corresponds to ``freqA`` and the second element corresponds
to ``freqB``
chanAB: list of float, optional
The pair of channels that will be used to select the nominal frequencies to be
used for frequency-differencing, where the first element corresponds to ``freqA``
and the second element corresponds to ``freqB``
"""
# check that channel and frequency nominal are in source_Sv
if "channel" not in source_Sv.coords:
raise ValueError("The Dataset defined by source_Sv must have channel as a coordinate!")
elif "frequency_nominal" not in source_Sv.variables:
raise ValueError(
"The Dataset defined by source_Sv must have frequency_nominal as a variable!"
)
# make sure that the channel and frequency_nominal values are not repeated in source_Sv
if len(set(source_Sv.channel.values)) < source_Sv.channel.size:
raise ValueError(
"The provided source_Sv contains repeated channel values, this is not allowed!"
)
if len(set(source_Sv.frequency_nominal.values)) < source_Sv.frequency_nominal.size:
raise ValueError(
"The provided source_Sv contains repeated frequency_nominal "
"values, this is not allowed!"
)
# check that the elements of freqAB are in frequency_nominal
if (freqAB is not None) and (not all([freq in source_Sv.frequency_nominal for freq in freqAB])):
raise ValueError(
"The provided list input freqAB contains values that "
"are not in the frequency_nominal variable!"
)
# check that the elements of chanAB are in channel
if (chanAB is not None) and (not all([chan in source_Sv.channel for chan in chanAB])):
raise ValueError(
"The provided list input chanAB contains values that are "
"not in the channel coordinate!"
)
def frequency_differencing(
source_Sv: Union[xr.Dataset, str, pathlib.Path],
storage_options: Optional[dict] = {},
freqAB: Optional[List[float]] = None,
chanAB: Optional[List[str]] = None,
operator: str = ">",
diff: Union[float, int] = None,
) -> xr.DataArray:
"""
Create a mask based on the differences of Sv values using a pair of
frequencies. This method is often referred to as the "frequency-differencing"
or "dB-differencing" method.
Parameters
----------
source_Sv: xr.Dataset or str or pathlib.Path
If a Dataset this value contains the Sv data to create a mask for,
else it specifies the path to a zarr or netcdf file containing
a Dataset. This input must correspond to a Dataset that has the
coordinate ``channel`` and variables ``frequency_nominal`` and ``Sv``.
storage_options: dict, optional
Any additional parameters for the storage backend, corresponding to the
path provided for ``source_Sv``
freqAB: list of float, optional
The pair of nominal frequencies to be used for frequency-differencing, where
the first element corresponds to ``freqA`` and the second element corresponds
to ``freqB``. Only one of ``freqAB`` and ``chanAB`` should be provided, and not both.
chanAB: list of strings, optional
The pair of channels that will be used to select the nominal frequencies to be
used for frequency-differencing, where the first element corresponds to ``freqA``
and the second element corresponds to ``freqB``. Only one of ``freqAB`` and ``chanAB``
should be provided, and not both.
operator: {">", "<", "<=", ">=", "=="}
The operator for the frequency-differencing
diff: float or int
The threshold of Sv difference between frequencies
Returns
-------
xr.DataArray
A DataArray containing the mask for the Sv data. Regions satisfying the thresholding
criteria are filled with ``True``, else the regions are filled with ``False``.
Raises
------
ValueError
If neither ``freqAB`` or ``chanAB`` are given
ValueError
If both ``freqAB`` and ``chanAB`` are given
TypeError
If any input is not of the correct type
ValueError
If either ``freqAB`` or ``chanAB`` are provided and the list
does not contain 2 distinct elements
ValueError
If ``freqAB`` contains values that are not contained in ``frequency_nominal``
ValueError
If ``chanAB`` contains values that not contained in ``channel``
ValueError
If ``operator`` is not one of the following: ``">", "<", "<=", ">=", "=="``
ValueError
If the path provided for ``source_Sv`` is not a valid path
ValueError
If ``freqAB`` or ``chanAB`` is provided and the Dataset produced by ``source_Sv``
does not contain the coordinate ``channel`` and variable ``frequency_nominal``
Notes
-----
This function computes the frequency differencing as follows:
``Sv_freqA - Sv_freqB operator diff``. Thus, if ``operator = "<"``
and ``diff = "5"`` the following would be calculated:
``Sv_freqA - Sv_freqB < 5``.
Examples
--------
Compute frequency-differencing mask using a mock Dataset and channel selection:
>>> n = 5 # set the number of ping times and range samples
...
>>> # create mock Sv data
>>> Sv_da = xr.DataArray(data=np.stack([np.arange(n**2).reshape(n,n), np.identity(n)]),
... coords={"channel": ['chan1', 'chan2'],
... "ping_time": np.arange(n), "range_sample":np.arange(n)})
...
>>> # obtain mock frequency_nominal data
>>> freq_nom = xr.DataArray(data=np.array([1.0, 2.0]),
... coords={"channel": ['chan1', 'chan2']})
...
>>> # construct mock Sv Dataset
>>> Sv_ds = xr.Dataset(data_vars={"Sv": Sv_da, "frequency_nominal": freq_nom})
...
>>> # compute frequency-differencing mask using channel names
>>> echopype.mask.frequency_differencing(source_Sv=mock_Sv_ds, storage_options={}, freqAB=None,
... chanAB = ['chan1', 'chan2'],
... operator = ">=", diff=10.0)
<xarray.DataArray 'mask' (ping_time: 5, range_sample: 5)>
array([[False, False, False, False, False],
[False, False, False, False, False],
[ True, True, True, True, True],
[ True, True, True, True, True],
[ True, True, True, True, True]])
Coordinates:
* ping_time (ping_time) int64 0 1 2 3 4
* range_sample (range_sample) int64 0 1 2 3 4
"""
# check that non-data related inputs were correctly provided
_check_freq_diff_non_data_inputs(freqAB, chanAB, operator, diff)
# validate the source_Sv type or path (if it is provided)
source_Sv, file_type = validate_source_ds_da(source_Sv, storage_options)
if isinstance(source_Sv, str):
# open up Dataset using source_Sv path
source_Sv = xr.open_dataset(source_Sv, engine=file_type, chunks={}, **storage_options)
# check the source_Sv with respect to channel and frequency_nominal
_check_source_Sv_freq_diff(source_Sv, freqAB, chanAB)
# determine chanA and chanB
if freqAB is not None:
# obtain position of frequency provided in frequency_nominal
freqA_pos = np.argwhere(source_Sv.frequency_nominal.values == freqAB[0]).flatten()[0]
freqB_pos = np.argwhere(source_Sv.frequency_nominal.values == freqAB[1]).flatten()[0]
# get channels corresponding to frequencies provided
chanA = str(source_Sv.channel.isel(channel=freqA_pos).values)
chanB = str(source_Sv.channel.isel(channel=freqB_pos).values)
else:
# get individual channels
chanA = chanAB[0]
chanB = chanAB[1]
# get the left-hand side of condition
lhs = source_Sv["Sv"].sel(channel=chanA) - source_Sv["Sv"].sel(channel=chanB)
# create mask using operator lookup table
da = xr.where(str2ops[operator](lhs, diff), True, False)
# assign a name to DataArray
da.name = "mask"
# assign provenance attributes
mask_attrs = {"mask_type": "frequency differencing"}
history_attr = (
f"{datetime.datetime.utcnow()} +00:00. "
"Mask created by mask.frequency_differencing. "
f"Operation: Sv['{chanA}'] - Sv['{chanB}'] {operator} {diff}"
)
da = da.assign_attrs({**mask_attrs, **{"history": history_attr}})
return da
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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,793 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/clean/noise_est.py | import numpy as np
from ..utils import uwa
class NoiseEst:
"""
Attributes
----------
ds_Sv : xr.Dataset
dataset containing ``Sv`` and ``echo_range`` [m]
ping_num : int
number of pings to obtain noise estimates
range_sample_num : int
number of samples along ``echo_range`` to obtain noise estimates
"""
def __init__(self, ds_Sv, ping_num, range_sample_num):
self.ds_Sv = ds_Sv
self.ping_num = ping_num
self.range_sample_num = range_sample_num
self.spreading_loss = None
self.absorption_loss = None
self.Sv_noise = None
self._compute_transmission_loss()
self._compute_power_cal()
def _compute_transmission_loss(self):
"""Compute transmission loss"""
if "sound_absorption" not in self.ds_Sv:
sound_absorption = uwa.calc_absorption(
frequency=self.ds_Sv.frequency_nominal,
temperature=self.ds_Sv["temperature"],
salinity=self.ds_Sv["salinity"],
pressure=self.ds_Sv["pressure"],
)
else:
sound_absorption = self.ds_Sv["sound_absorption"]
# Transmission loss
self.spreading_loss = 20 * np.log10(
self.ds_Sv["echo_range"].where(self.ds_Sv["echo_range"] >= 1, other=1)
)
self.absorption_loss = 2 * sound_absorption * self.ds_Sv["echo_range"]
def _compute_power_cal(self):
"""Compute calibrated power without TVG, linear domain"""
self.power_cal = 10 ** (
(self.ds_Sv["Sv"] - self.spreading_loss - self.absorption_loss) / 10
)
def estimate_noise(self, noise_max=None):
"""Estimate noise from a collected of pings
Parameters
----------
noise_max : Union[int, float]
the upper limit for background noise expected under the operating conditions
"""
power_cal_binned_avg = 10 * np.log10( # binned averages of calibrated power
self.power_cal.coarsen(
ping_time=self.ping_num,
range_sample=self.range_sample_num,
boundary="pad",
).mean()
)
noise = power_cal_binned_avg.min(dim="range_sample", skipna=True)
# align ping_time to first of each ping collection
noise["ping_time"] = self.power_cal["ping_time"][:: self.ping_num]
if noise_max is not None:
noise = noise.where(noise < noise_max, noise_max) # limit max noise level
self.Sv_noise = (
noise.reindex(
{"ping_time": self.power_cal["ping_time"]}, method="ffill"
) # forward fill empty index
+ self.spreading_loss
+ self.absorption_loss
)
def remove_noise(self, noise_max=None, SNR_threshold=3):
"""
Remove noise by using estimates of background noise
from mean calibrated power of a collection of pings.
This method adds two data variables to the input ``ds_Sv``:
- corrected Sv (``Sv_corrected``)
- noise estimates (``Sv_noise``)
Reference: De Robertis & Higginbottom. 2007.
A post-processing technique to estimate the signal-to-noise ratio
and remove echosounder background noise.
ICES Journal of Marine Sciences 64(6): 1282–1291.
Parameters
----------
noise_max : float
the upper limit for background noise expected under the operating conditions
SNR_threshold : float
acceptable signal-to-noise ratio, default to 3 dB
"""
# Compute Sv_noise
self.estimate_noise(noise_max=noise_max)
# Sv corrected for noise
# linear domain
fac = 10 ** (self.ds_Sv["Sv"] / 10) - 10 ** (self.Sv_noise / 10)
Sv_corr = 10 * np.log10(fac.where(fac > 0, other=np.nan))
Sv_corr = Sv_corr.where(
Sv_corr - self.Sv_noise > SNR_threshold, other=np.nan
) # other=-999 (from paper)
# Assemble output dataset
def add_attrs(sv_type, da):
da.attrs = {
"long_name": f"Volume backscattering strength, {sv_type} (Sv re 1 m-1)",
"units": "dB",
"actual_range": [
round(float(da.min().values), 2),
round(float(da.max().values), 2),
],
"noise_ping_num": self.ping_num,
"noise_range_sample_num": self.range_sample_num,
"SNR_threshold": SNR_threshold,
"noise_max": noise_max,
}
self.ds_Sv["Sv_noise"] = self.Sv_noise
add_attrs("noise", self.ds_Sv["Sv_noise"])
self.ds_Sv["Sv_corrected"] = Sv_corr
add_attrs("corrected", self.ds_Sv["Sv_corrected"])
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,794 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/qc/__init__.py | from .api import coerce_increasing_time, exist_reversed_time
__all__ = ["coerce_increasing_time", "exist_reversed_time"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": 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"/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], 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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,795 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/utils/test_processinglevels_integration.py | import sys
import pytest
import numpy as np
import xarray as xr
import echopype as ep
pytestmark = pytest.mark.skipif(sys.platform == "win32", reason="Test data not available on windows tests")
@pytest.mark.parametrize(
["sonar_model", "path_model", "raw_and_xml_paths", "extras"],
[
pytest.param(
"EK60",
"EK60",
("Winter2017-D20170115-T150122.raw", None),
{},
# marks=pytest.mark.skipif(sys.platform == "win32", reason="Test data not available on windows tests"),
),
pytest.param(
"AZFP",
"AZFP",
("17082117.01A", "17041823.XML"),
{"longitude": -60.0, "latitude": 45.0, "salinity": 27.9, "pressure": 59},
# marks=pytest.mark.skipif(sys.platform == "win32", reason="Test data not available on windows tests"),
),
],
)
def test_raw_to_mvbs(
sonar_model,
path_model,
raw_and_xml_paths,
extras,
test_path
):
# Prepare the Sv dataset
raw_path = test_path[path_model] / raw_and_xml_paths[0]
if raw_and_xml_paths[1]:
xml_path = test_path[path_model] / raw_and_xml_paths[1]
else:
xml_path = None
def _presence_test(test_ds, processing_level):
assert "processing_level" in test_ds.attrs
assert "processing_level_url" in test_ds.attrs
assert test_ds.attrs["processing_level"] == processing_level
def _absence_test(test_ds):
assert "processing_level" not in test_ds.attrs
assert "processing_level_url" not in test_ds.attrs
# ---- Convert raw file and update_platform
def _var_presence_notnan_test(name):
if name in ed['Platform'].data_vars and not ed["Platform"][name].isnull().all():
return True
else:
return False
ed = ep.open_raw(raw_path, xml_path=xml_path, sonar_model=sonar_model)
if _var_presence_notnan_test("longitude") and _var_presence_notnan_test("latitude"):
_presence_test(ed["Top-level"], "Level 1A")
elif "longitude" in extras and "latitude" in extras:
_absence_test(ed["Top-level"])
point_ds = xr.Dataset(
{
"latitude": (["time"], np.array([float(extras["latitude"])])),
"longitude": (["time"], np.array([float(extras["longitude"])])),
},
coords={
"time": (["time"], np.array([ed["Sonar/Beam_group1"]["ping_time"].values.min()]))
},
)
ed.update_platform(point_ds, variable_mappings={"latitude": "latitude", "longitude": "longitude"})
_presence_test(ed["Top-level"], "Level 1A")
else:
_absence_test(ed["Top-level"])
raise RuntimeError(
"Platform latitude and longitude are not present and cannot be added "
"using update_platform based on test raw file and included parameters."
)
# ---- Calibrate and add_latlon
env_params = None
if sonar_model == "AZFP":
# AZFP data require external salinity and pressure
env_params = {
"temperature": ed["Environment"]["temperature"].values.mean(),
"salinity": extras["salinity"],
"pressure": extras["pressure"],
}
ds = ep.calibrate.compute_Sv(echodata=ed, env_params=env_params)
_absence_test(ds)
Sv_ds = ep.consolidate.add_location(ds=ds, echodata=ed)
assert "longitude" in Sv_ds.data_vars and "latitude" in Sv_ds.data_vars
_presence_test(Sv_ds, "Level 2A")
# ---- Noise removal
denoised_ds = ep.clean.remove_noise(Sv_ds, ping_num=10, range_sample_num=20)
_presence_test(denoised_ds, "Level 2B")
# ---- apply_mask based on frequency differencing
def _freqdiff_applymask(test_ds):
# frequency_differencing expects a dataarray variable named "Sv". For denoised Sv,
# rename Sv to Sv_raw and Sv_corrected to Sv before passing ds to frequency_differencing
if "Sv_corrected" in test_ds.data_vars:
out_ds = test_ds.rename_vars(name_dict={"Sv": "Sv_raw", "Sv_corrected": "Sv"})
else:
out_ds = test_ds
freqAB = list(out_ds.frequency_nominal.values[:2])
freqdiff_da = ep.mask.frequency_differencing(source_Sv=out_ds, freqAB=freqAB, operator=">", diff=5)
# Apply mask to multi-channel Sv
return ep.mask.apply_mask(source_ds=out_ds, var_name="Sv", mask=freqdiff_da)
# On Sv w/o noise removal
ds = _freqdiff_applymask(Sv_ds)
_presence_test(ds, "Level 3A")
# On denoised Sv
ds = _freqdiff_applymask(denoised_ds)
_presence_test(ds, "Level 3B")
# ---- Compute MVBS
# compute_MVBS expects a variable named "Sv"
# No product level is assigned because at present compute_MVBS drops the lat/lon data
# associated with the input Sv dataset
# ds = ds.rename_vars(name_dict={"Sv": "Sv_unmasked", "Sv_ch0": "Sv"})
mvbs_ds = ep.commongrid.compute_MVBS(ds, range_meter_bin=30, ping_time_bin='1min')
_absence_test(mvbs_ds)
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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,796 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/metrics/test_metrics_summary_statistics.py | import xarray as xr
import numpy as np
import pandas as pd
from echopype.metrics.summary_statistics import (
delta_z,
convert_to_linear,
abundance,
center_of_mass,
dispersion,
evenness,
aggregation,
)
# Utility Function
def create_test_ds(Sv, echo_range):
freq = [30]
time = pd.date_range("2021-08-28", periods=2)
reference_time = pd.Timestamp("2021-08-27")
r_b = [0, 1, 2]
testDS = xr.Dataset(
data_vars=dict(
Sv=(["frequency", "ping_time", "range_sample"], Sv),
echo_range=(["frequency", "ping_time", "range_sample"], echo_range),
),
coords={
'frequency': xr.DataArray(
freq,
name='frequency',
dims=['frequency'],
attrs={'units': 'kHz'},
),
'ping_time': xr.DataArray(
time,
name='ping_time',
dims=['ping_time'],
),
'range_sample': xr.DataArray(
r_b,
name='range_sample',
dims=['range_sample'],
),
},
)
return testDS
# Test Functions
def test_abundance():
"""Compares summary_statistics.py calculation of abundance with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
ab_ds1 = create_test_ds(Sv, echo_range)
ab_ds1_SOL = np.array([[60.04321374, 30.41392685]])
assert np.allclose(
abundance(ab_ds1), ab_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
def test_center_of_mass():
"""Compares summary_statistics.py calculation of center_of_mass with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
cm_ds1 = create_test_ds(Sv, echo_range)
cm_ds1_SOL = np.array([[2.99009901, 3.90909090]])
assert np.allclose(
center_of_mass(cm_ds1), cm_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
def test_inertia():
"""Compares summary_statistics.py calculation of inertia with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
in_ds1 = create_test_ds(Sv, echo_range)
in_ds1_SOL = np.array([[0.00980296, 0.08264463]])
assert np.allclose(
dispersion(in_ds1), in_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
def test_evenness():
"""Compares summary_statistics.py calculation of evenness with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
ev_ds1 = create_test_ds(Sv, echo_range)
ev_ds1_SOL = np.array([[1.019998, 1.198019802]])
assert np.allclose(
evenness(ev_ds1), ev_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
def test_aggregation():
"""Compares summary_statistics.py calculation of aggregation with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
ag_ds1 = create_test_ds(Sv, echo_range)
ag_ds1_SOL = np.array([[0.9803940792, 0.8347107438]])
assert np.allclose(
aggregation(ag_ds1), ag_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
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"/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,797 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/testing.py | from pathlib import Path
HERE = Path(__file__).parent.absolute()
TEST_DATA_FOLDER = HERE / "test_data"
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,798 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/__init__.py | from __future__ import absolute_import, division, print_function
from _echopype_version import version as __version__ # noqa
from . import calibrate, clean, commongrid, consolidate, mask, utils
from .convert.api import open_raw
from .echodata.api import open_converted
from .echodata.combine import combine_echodata
from .utils.io import init_ep_dir
from .utils.log import verbose
# Turn off verbosity for echopype
verbose(override=True)
init_ep_dir()
__all__ = [
"calibrate",
"clean",
"combine_echodata",
"commongrid",
"consolidate",
"mask",
"metrics",
"open_converted",
"open_raw",
"utils",
"verbose",
]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,799 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/env_params_old.py | from typing import Dict, List
import numpy as np
import scipy.interpolate
import xarray as xr
from typing_extensions import Literal
DataKind = Literal["stationary", "mobile", "organized"]
InterpMethod = Literal["linear", "nearest", "zero", "slinear", "quadratic", "cubic"]
ExtrapMethod = Literal["linear", "nearest"]
VALID_INTERP_METHODS: Dict[DataKind, List[InterpMethod]] = {
"stationary": ["linear", "nearest", "zero", "slinear", "quadratic", "cubic"],
"mobile": ["linear", "nearest", "cubic"],
"organized": ["linear", "nearest"],
}
class EnvParams:
def __init__(
self,
env_params: xr.Dataset,
data_kind: DataKind,
interp_method: InterpMethod = "linear",
extrap_method: ExtrapMethod = "linear",
):
"""
Class to hold and interpolate external environmental data for calibration purposes.
This class can be used as the `env_params` parameter in `echopype.calibrate.compute_Sv`
or `echopype.calibrate.compute_TS`. It is intended to be used with environmental parameters
indexed by time. Environmental parameters will be interpolated onto dimensions within
the Platform group of the `EchoData` object being used for calibration.
Parameters
----------
env_params : xr.Dataset
The environmental parameters to use for calibration. This data will be interpolated with
a provided `EchoData` object.
When `data_kind` is `"stationary"`, env_params must have a coordinate `"time3"`.
When `data_kind` is `"mobile"`, env_params must have coordinates `"latitude"`
and `"longitude"`.
When `data_kind` is `"organized"`, env_params must have coordinates `"time"`,
`"latitude"`, and `"longitude"`. This `data_kind` is not currently supported.
data_kind : {"stationary", "mobile", "organized"}
The type of the environmental parameters.
`"stationary"`: environmental parameters from a fixed location
(for example, a single CTD).
`"mobile"` environmental parameters from a moving location (for example, a ship).
`"organized"`: environmental parameters from many fixed locations
(for example, multiple CTDs).
interp_method: {"linear", "nearest", "zero", "slinear", "quadratic", "cubic"}
Method for interpolation of environmental parameters with the data from the
provided `EchoData` object.
When `data_kind` is `"stationary"`, valid `interp_method`s are `"linear"`, `"nearest"`,
`"zero"`, `"slinear"`, `"quadratic"`, and `"cubic"`
(see <https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.interpolate.interp1d.html>).
When `data_kind` is `"mobile"`, valid `interp_method`s are `"linear"`, `"nearest"`, and `"cubic"`
(see <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html>).
When `data_kind` is `"organized"`, valid `interp_method`s are `"linear"` and `"nearest"`
(see <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html>).
extrap_method: {"linear", "nearest"}
Method for extrapolation of environmental parameters with the data from the
provided `EchoData` object. Currently only supported when `data_kind` is `"stationary"`.
Notes
-----
Currently cases where `data_kind` is `"organized"` are not supported; support will be added
in a future version.
Examples
--------
>>> env_params = xr.open_dataset("env_params.nc")
>>> EnvParams(env_params, data_kind="mobile", interp_method="linear")
>>> echopype.calibrate.compute_Sv(echodata, env_params=env_params)
""" # noqa
if interp_method not in VALID_INTERP_METHODS[data_kind]:
raise ValueError(f"invalid interp_method {interp_method} for data_kind {data_kind}")
self.env_params = env_params
self.data_kind = data_kind
self.interp_method = interp_method
self.extrap_method = extrap_method
def _apply(self, echodata) -> Dict[str, xr.DataArray]:
if self.data_kind == "stationary":
dims = ["time3"]
elif self.data_kind == "mobile":
dims = ["latitude", "longitude"]
elif self.data_kind == "organized":
dims = ["time", "latitude", "longitude"]
else:
raise ValueError("invalid data_kind")
for dim in dims:
if dim not in echodata["Platform"]:
raise ValueError(
f"could not interpolate env_params; EchoData is missing dimension {dim}"
)
env_params = self.env_params
if self.data_kind == "mobile":
if np.isnan(echodata["Platform"]["time1"]).all():
raise ValueError("cannot perform mobile interpolation without time1")
# only grab needed variables for the interpolation
platform_data = echodata["Platform"][["latitude", "longitude"]]
# compute_range needs indexing by ping_time
interp_plat = platform_data.interp(
{"time1": echodata["Sonar/Beam_group1"]["ping_time"]}
)
result = {}
for var, values in env_params.data_vars.items():
points = np.column_stack(
(env_params["latitude"].data, env_params["longitude"].data)
)
values = values.data
xi = np.column_stack(
(
interp_plat["latitude"].data,
interp_plat["longitude"].data,
)
)
interp = scipy.interpolate.griddata(points, values, xi, method=self.interp_method)
result[var] = ("time1", interp)
env_params = xr.Dataset(
# we expect env_params to have coordinate time1
data_vars=result,
coords={"time1": interp_plat["ping_time"].data},
)
else:
# TODO: organized case
min_max = {
dim: {"min": env_params[dim].min(), "max": env_params[dim].max()} for dim in dims
}
extrap = env_params.interp(
{dim: echodata["Platform"][dim].data for dim in dims},
method=self.extrap_method,
# scipy interp uses "extrapolate" but scipy interpn uses None
kwargs={"fill_value": "extrapolate" if len(dims) == 1 else None},
)
# only keep unique indexes; xarray requires that indexes be unique
extrap_unique_idx = {dim: np.unique(extrap[dim], return_index=True)[1] for dim in dims}
extrap = extrap.isel(**extrap_unique_idx)
interp = env_params.interp(
{dim: echodata["Platform"][dim].data for dim in dims},
method=self.interp_method,
)
interp_unique_idx = {dim: np.unique(interp[dim], return_index=True)[1] for dim in dims}
interp = interp.isel(**interp_unique_idx)
if self.extrap_method is not None:
less = extrap.sel(
{dim: extrap[dim][extrap[dim] < min_max[dim]["min"]] for dim in dims}
)
middle = interp.sel(
{
dim: interp[dim][
np.logical_and(
interp[dim] >= min_max[dim]["min"],
interp[dim] <= min_max[dim]["max"],
)
]
for dim in dims
}
)
greater = extrap.sel(
{dim: extrap[dim][extrap[dim] > min_max[dim]["max"]] for dim in dims}
)
# remove empty datasets (xarray does not allow any dims from any datasets
# to be length 0 in combine_by_coords)
non_zero_dims = [
ds
for ds in (less, middle, greater)
if all(dim_len > 0 for dim_len in ds.dims.values())
]
env_params = xr.combine_by_coords(non_zero_dims)
# if self.data_kind == "organized":
# # get platform latitude and longitude indexed by ping_time
# interp_plat = echodata["Platform"].interp(
# {"time": echodata["Platform"]["ping_time"]}
# )
# # get env_params latitude and longitude indexed by ping_time
# env_params = env_params.interp(
# {
# "latitude": interp_plat["latitude"],
# "longitude": interp_plat["longitude"],
# }
# )
if self.data_kind == "stationary":
# renaming time3 (from Platform group) to time1 because we expect
# environmental parameters to have dimension time1
return {
var: env_params[var].rename({"time3": "time1"})
for var in ("temperature", "salinity", "pressure")
}
else:
return {var: env_params[var] for var in ("temperature", "salinity", "pressure")}
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,800 | OSOceanAcoustics/echopype | refs/heads/main | /.ci_helpers/check-version.py | import argparse
import sys
import echopype
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Check current echopype version.")
parser.add_argument(
"expected_version",
type=str,
nargs="?",
default="0.5.0",
help="Expected Echopype Version to check",
)
args = parser.parse_args()
expected_version = args.expected_version
installed_version = echopype.__version__
if installed_version != expected_version:
print(
f"!! Installed version {installed_version} does not match expected version {expected_version}." # noqa
)
sys.exit(1)
else:
print(f"Installed version {installed_version} is expected.")
sys.exit(0)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,801 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/utils/coding.py | from re import search
from typing import Any, Dict, Tuple
import numpy as np
import xarray as xr
import zarr
from dask.array.core import auto_chunks
from dask.utils import parse_bytes
from xarray import coding
DEFAULT_TIME_ENCODING = {
"units": "seconds since 1900-01-01T00:00:00+00:00",
"calendar": "gregorian",
"_FillValue": np.nan,
"dtype": np.dtype("float64"),
}
COMPRESSION_SETTINGS = {
"netcdf4": {"zlib": True, "complevel": 4},
# zarr compressors were chosen based on xarray results
"zarr": {
"float": {"compressor": zarr.Blosc(cname="zstd", clevel=3, shuffle=2)},
"int": {"compressor": zarr.Blosc(cname="lz4", clevel=5, shuffle=1, blocksize=0)},
"string": {"compressor": zarr.Blosc(cname="lz4", clevel=5, shuffle=1, blocksize=0)},
"time": {"compressor": zarr.Blosc(cname="lz4", clevel=5, shuffle=1, blocksize=0)},
},
}
DEFAULT_ENCODINGS = {
"ping_time": DEFAULT_TIME_ENCODING,
"ping_time_transmit": DEFAULT_TIME_ENCODING,
"time1": DEFAULT_TIME_ENCODING,
"time2": DEFAULT_TIME_ENCODING,
"time3": DEFAULT_TIME_ENCODING,
"time4": DEFAULT_TIME_ENCODING,
"time5": DEFAULT_TIME_ENCODING,
}
EXPECTED_VAR_DTYPE = {
"channel": np.str_,
"cal_channel_id": np.str_,
"beam": np.str_,
"channel_mode": np.byte,
"beam_stabilisation": np.byte,
"non_quantitative_processing": np.int16,
} # channel name # beam name
PREFERRED_CHUNKS = "preferred_chunks"
def sanitize_dtypes(ds: xr.Dataset) -> xr.Dataset:
"""
Validates and fixes data type for expected variables
"""
if isinstance(ds, xr.Dataset):
for name, var in ds.variables.items():
if name in EXPECTED_VAR_DTYPE:
expected_dtype = EXPECTED_VAR_DTYPE[name]
elif np.issubdtype(var.dtype, np.object_):
# Defaulting to strings dtype for object data types
expected_dtype = np.str_
else:
# For everything else, this should be the same
expected_dtype = var.dtype
if not np.issubdtype(var.dtype, expected_dtype):
ds[name] = var.astype(expected_dtype)
return ds
def _encode_dataarray(da, dtype):
"""Encodes and decode datetime64 array similar to writing to file"""
if da.size == 0:
return da
read_encoding = {
"units": "seconds since 1900-01-01T00:00:00+00:00",
"calendar": "gregorian",
}
if dtype in [np.float64, np.int64]:
encoded_data = da
else:
# fmt: off
encoded_data, _, _ = coding.times.encode_cf_datetime(
da, **read_encoding
)
# fmt: on
return coding.times.decode_cf_datetime(encoded_data, **read_encoding)
def _get_auto_chunk(
variable: xr.DataArray, chunk_size: "int | str | float" = "100MB"
) -> Tuple[int]:
"""
Calculate default chunks for a data array based on desired chunk size
Parameters
----------
variable : xr.DataArray
The data array variable to be calculated
chunk_size : int or str or float
The desired max chunk size for the array.
Default is 100MB
Returns
-------
tuple
The chunks
"""
auto_tuple = tuple(["auto" for i in variable.shape])
chunks = auto_chunks(auto_tuple, variable.shape, chunk_size, variable.dtype)
return tuple([c[0] if isinstance(c, tuple) else c for c in chunks])
def set_time_encodings(ds: xr.Dataset) -> xr.Dataset:
"""
Set the default encoding for variables.
"""
new_ds = ds.copy(deep=True)
for var, encoding in DEFAULT_ENCODINGS.items():
if var in new_ds:
da = new_ds[var].copy()
# Process all variable names matching the patterns *_time* or time<digits>
# Examples: ping_time, ping_time_2, time1, time2
if bool(search(r"_time|^time[\d]+$", var)):
new_ds[var] = xr.apply_ufunc(
_encode_dataarray,
da,
keep_attrs=True,
kwargs={"dtype": da.dtype},
)
new_ds[var].encoding = encoding
return new_ds
def get_zarr_compression(var: xr.Variable, compression_settings: dict) -> dict:
"""Returns the proper zarr compressor for a given variable type"""
if np.issubdtype(var.dtype, np.floating):
return compression_settings["float"]
elif np.issubdtype(var.dtype, np.integer):
return compression_settings["int"]
elif np.issubdtype(var.dtype, np.str_):
return compression_settings["string"]
elif np.issubdtype(var.dtype, np.datetime64):
return compression_settings["time"]
else:
raise NotImplementedError(f"Zarr Encoding for dtype = {var.dtype} has not been set!")
def set_zarr_encodings(
ds: xr.Dataset, compression_settings: dict, chunk_size: str = "100MB", ctol: str = "10MB"
) -> dict:
"""
Obtains all variable encodings based on zarr default values
Parameters
----------
ds : xr.Dataset
The dataset object to generate encoding for
compression_settings : dict
The compression settings dictionary
chunk_size : dict
The desired chunk size
ctol : dict
The chunk size tolerance before rechunking
Returns
-------
dict
The encoding dictionary
"""
# create zarr specific encoding
encoding = dict()
for name, val in ds.variables.items():
encoding[name] = {**val.encoding}
encoding[name].update(get_zarr_compression(val, compression_settings))
# Always optimize chunk if not specified already
# user can specify desired chunk in encoding
existing_chunks = encoding[name].get("chunks", None)
optimal_chunk_size = parse_bytes(chunk_size)
chunk_size_tolerance = parse_bytes(ctol)
if len(val.shape) > 0:
rechunk = True
if existing_chunks is not None:
# Perform chunk optimization
# 1. Get the chunk total from existing chunks
chunk_total = np.prod(existing_chunks) * val.dtype.itemsize
# 2. Get chunk size difference from the optimal chunk size
chunk_diff = optimal_chunk_size - chunk_total
# 3. Check difference from tolerance, if diff is less than
# tolerance then no need to rechunk
if chunk_diff < chunk_size_tolerance:
rechunk = False
chunks = existing_chunks
if rechunk:
# Use dask auto chunk to determine the optimal chunk
# spread for optimal chunk size
chunks = _get_auto_chunk(val, chunk_size=chunk_size)
encoding[name]["chunks"] = chunks
return encoding
def set_netcdf_encodings(
ds: xr.Dataset,
compression_settings: Dict[str, Any] = {},
) -> Dict[str, Dict[str, Any]]:
"""
Obtains all variables encodings based on netcdf default values
Parameters
----------
ds : xr.Dataset
The dataset object to generate encoding for
compression_settings : dict
The compression settings dictionary
Returns
-------
dict
The final encoding values for dataset variables
"""
encoding = dict()
for name, val in ds.variables.items():
encoding[name] = {**val.encoding}
if np.issubdtype(val.dtype, np.str_):
encoding[name].update(
{
"zlib": False,
}
)
elif compression_settings:
encoding[name].update(compression_settings)
else:
encoding[name].update(COMPRESSION_SETTINGS["netcdf4"])
return encoding
def set_storage_encodings(ds: xr.Dataset, compression_settings: dict, engine: str) -> dict:
"""
Obtains the appropriate zarr or netcdf specific encodings for
each variable in ``ds``.
"""
if compression_settings is not None:
if engine == "zarr":
encoding = set_zarr_encodings(ds, compression_settings)
elif engine == "netcdf4":
encoding = set_netcdf_encodings(ds, compression_settings)
else:
raise RuntimeError(f"Obtaining encodings for the engine {engine} is not allowed.")
else:
encoding = dict()
return encoding
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,802 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/ek80_complex.py | from collections import defaultdict
from typing import Dict, Literal, Optional, Union
import numpy as np
import xarray as xr
from scipy import signal
from ..convert.set_groups_ek80 import DECIMATION, FILTER_IMAG, FILTER_REAL
def tapered_chirp(
fs,
transmit_duration_nominal,
slope,
transmit_frequency_start,
transmit_frequency_stop,
):
"""
Create the chirp replica following implementation from Lars Anderson.
Ref source: https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf/blob/main/Core/Calculation.py # noqa
"""
tau = transmit_duration_nominal
f0 = transmit_frequency_start
f1 = transmit_frequency_stop
nsamples = int(np.floor(tau * fs))
t = np.linspace(0, nsamples - 1, num=nsamples) * 1 / fs
a = np.pi * (f1 - f0) / tau
b = 2 * np.pi * f0
y = np.cos(a * t * t + b * t)
L = int(np.round(tau * fs * slope * 2.0)) # Length of hanning window
w = 0.5 * (1.0 - np.cos(2.0 * np.pi * np.arange(0, L, 1) / (L - 1)))
N = len(y)
w1 = w[0 : int(len(w) / 2)]
w2 = w[int(len(w) / 2) : -1]
i0 = 0
i1 = len(w1)
i2 = N - len(w2)
i3 = N
y[i0:i1] = y[i0:i1] * w1
y[i2:i3] = y[i2:i3] * w2
return y / np.max(y), t # amplitude needs to be normalized
def filter_decimate_chirp(coeff_ch: Dict, y_ch: np.array, fs: float):
"""Filter and decimate the transmit replica for one channel.
Parameters
----------
coeff_ch : dict
a dictionary containing filter coefficients and decimation factors for ``ch_id``
y_ch : np.array
chirp from _tapered_chirp
fs : float
system sampling frequency [Hz]
"""
# Get values
# WBT filter and decimation
ytx_wbt = signal.convolve(y_ch, coeff_ch["wbt_fil"])
ytx_wbt_deci = ytx_wbt[0 :: coeff_ch["wbt_decifac"]]
# PC filter and decimation
ytx_pc = signal.convolve(ytx_wbt_deci, coeff_ch["pc_fil"])
ytx_pc_deci = ytx_pc[0 :: coeff_ch["pc_decifac"]]
ytx_pc_deci_time = (
np.arange(ytx_pc_deci.size) * 1 / fs * coeff_ch["wbt_decifac"] * coeff_ch["pc_decifac"]
)
return ytx_pc_deci, ytx_pc_deci_time
def get_vend_filter_EK80(
vend: xr.Dataset,
channel_id: str,
filter_name: Literal["WBT", "PC"],
param_type: Literal["coeff", "decimation"],
) -> Optional[Union[np.ndarray, int]]:
"""
Get filter coefficients stored in the Vendor_specific group attributes.
Parameters
----------
vend: xr.Dataset
An xr.Dataset from EchoData["Vendor_specific"]
channel_id : str
channel id for which the param to be retrieved
filter_name : str
name of filter coefficients to retrieve
param_type : str
'coeff' or 'decimation'
Returns
-------
np.ndarray or int or None
The filter coefficient or the decimation factor
"""
var_imag = f"{filter_name}_{FILTER_IMAG}"
var_real = f"{filter_name}_{FILTER_REAL}"
var_df = f"{filter_name}_{DECIMATION}"
# if the variables are not in the dataset, simply return None
if not all([var in vend for var in [var_imag, var_real, var_df]]):
return None
# Select the channel requested
sel_vend = vend.sel(channel=channel_id)
if param_type == "coeff":
# Compute complex number from imaginary and real parts
v_complex = sel_vend[var_real] + 1j * sel_vend[var_imag]
# Drop nan fillers and get the values
v = v_complex.dropna(dim=f"{filter_name}_filter_n").values
return v
else:
# Get the decimation value
return sel_vend[var_df].values
def get_filter_coeff(vend: xr.Dataset) -> Dict:
"""
Get WBT and PC filter coefficients for constructing the transmit replica.
Parameters
----------
vend: xr.Dataset
An xr.Dataset from EchoData["Vendor_specific"]
Returns
-------
dict
A dictionary indexed by ``channel`` and values being dictionaries containing
filter coefficients and decimation factors for constructing the transmit replica.
"""
coeff = defaultdict(dict)
for ch_id in vend["channel"].values:
# filter coefficients and decimation factor
coeff[ch_id]["wbt_fil"] = get_vend_filter_EK80(vend, ch_id, "WBT", "coeff")
coeff[ch_id]["pc_fil"] = get_vend_filter_EK80(vend, ch_id, "PC", "coeff")
coeff[ch_id]["wbt_decifac"] = get_vend_filter_EK80(vend, ch_id, "WBT", "decimation")
coeff[ch_id]["pc_decifac"] = get_vend_filter_EK80(vend, ch_id, "PC", "decimation")
return coeff
def get_tau_effective(
ytx_dict: Dict[str, np.array],
fs_deci_dict: Dict[str, float],
waveform_mode: str,
channel: xr.DataArray,
ping_time: xr.DataArray,
):
"""Compute effective pulse length.
Parameters
----------
ytx_dict : dict
A dict of transmit signals, with keys being the ``channel`` and
values being either a vector when the transmit signals are identical across all pings
or a 2D array when the transmit signals vary across ping
fs_deci_dict : dict
A dict of sampling frequency of the decimated (recorded) signal,
with keys being the ``channel``
waveform_mode : str
``CW`` for CW-mode samples, either recorded as complex or power samples
``BB`` for BB-mode samples, recorded as complex samples
"""
tau_effective = {}
for ch, ytx in ytx_dict.items():
if waveform_mode == "BB":
ytxa = signal.convolve(ytx, np.flip(np.conj(ytx))) / np.linalg.norm(ytx) ** 2
ptxa = np.abs(ytxa) ** 2
elif waveform_mode == "CW":
ptxa = np.abs(ytx) ** 2 # energy of transmit signal
tau_effective[ch] = ptxa.sum() / (ptxa.max() * fs_deci_dict[ch])
# set up coordinates
if len(ytx.shape) == 1: # ytx is a vector (transmit signals are identical across pings)
coords = {"channel": channel}
elif len(ytx.shape) == 2: # ytx is a matrix (transmit signals vary across pings)
coords = {"channel": channel, "ping_time": ping_time}
vals = np.array(list(tau_effective.values())).squeeze()
if vals.size == 1:
vals = np.expand_dims(vals, axis=0)
tau_effective = xr.DataArray(
data=vals,
coords=coords,
)
return tau_effective
def get_transmit_signal(
beam: xr.Dataset,
coeff: Dict,
waveform_mode: str,
fs: Union[float, xr.DataArray],
):
"""Reconstruct transmit signal and compute effective pulse length.
Parameters
----------
beam : xr.Dataset
EchoData["Sonar/Beam_group1"] selected with channel subset
coeff : dict
a dictionary indexed by ``channel`` and values being dictionaries containing
filter coefficients and decimation factors for constructing the transmit replica.
waveform_mode : str
``CW`` for CW-mode samples, either recorded as complex or power samples
``BB`` for BB-mode samples, recorded as complex samples
Return
------
y_all
Transmit replica (BB: broadband chirp, CW: constant frequency sinusoid)
y_time_all
Timestamp for the transmit replica
"""
# Make sure it is BB mode data
# This is already checked in calibrate_ek
# but keeping this here for use as standalone function
if waveform_mode == "BB" and np.all(beam["transmit_type"] == "CW"):
raise TypeError("File does not contain BB mode complex samples!")
# Generate all transmit replica
y_all = {}
y_time_all = {}
# TODO: expand to deal with the case with varying tx param across ping_time
tx_param_names = [
"transmit_duration_nominal",
"slope",
"transmit_frequency_start",
"transmit_frequency_stop",
]
for ch in beam["channel"].values:
tx_params = {}
for p in tx_param_names:
tx_params[p] = np.unique(beam[p].sel(channel=ch))
if tx_params[p].size != 1:
raise TypeError("File contains changing %s!" % p)
fs_chan = fs.sel(channel=ch).data if isinstance(fs, xr.DataArray) else fs
tx_params["fs"] = fs_chan
y_ch, _ = tapered_chirp(**tx_params)
# Filter and decimate chirp template
y_ch, y_tmp_time = filter_decimate_chirp(coeff_ch=coeff[ch], y_ch=y_ch, fs=fs_chan)
# Fill into output dict
y_all[ch] = y_ch
y_time_all[ch] = y_tmp_time
return y_all, y_time_all
def compress_pulse(backscatter: xr.DataArray, chirp: Dict) -> xr.DataArray:
"""Perform pulse compression on the backscatter data.
Parameters
----------
backscatter : xr.DataArray
complex backscatter samples
chirp : dict
transmit chirp replica indexed by ``channel``
Returns
-------
xr.DataArray
A data array containing pulse compression output.
"""
pc_all = []
for chan in backscatter["channel"]:
backscatter_chan = backscatter.sel(channel=chan).dropna(dim="beam", how="all")
tx = chirp[str(chan.values)]
replica = np.flipud(np.conj(tx))
pc = xr.apply_ufunc(
lambda m: np.apply_along_axis(
lambda m: (signal.convolve(m, replica, mode="full")[tx.size - 1 :]),
axis=2,
arr=m,
),
backscatter_chan,
input_core_dims=[["range_sample"]],
output_core_dims=[["range_sample"]],
# exclude_dims={"range_sample"},
)
pc_all.append(pc)
pc_all = xr.DataArray(
pc_all,
coords={
"channel": backscatter["channel"],
"ping_time": backscatter["ping_time"],
"beam": backscatter["beam"],
"range_sample": backscatter["range_sample"],
},
)
return pc_all
def get_norm_fac(chirp: Dict) -> xr.DataArray:
"""
Get normalization factor from the chirp dictionary.
Parameters
----------
chirp : dict
transmit chirp replica indexed by ``channel``
Returns
-------
xr.DataArray
A data array containing the normalization factor, with channel coordinate
"""
norm_fac = []
ch_all = []
for ch, tx in chirp.items():
norm_fac.append(np.linalg.norm(tx) ** 2)
ch_all.append(ch)
return xr.DataArray(norm_fac, coords={"channel": ch_all})
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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,803 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/consolidate/__init__.py | from .api import add_depth, add_location, add_splitbeam_angle, swap_dims_channel_frequency
__all__ = ["swap_dims_channel_frequency", "add_depth", "add_location", "add_splitbeam_angle"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,804 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/commongrid/api.py | """
Functions for enhancing the spatial and temporal coherence of data.
"""
import numpy as np
import pandas as pd
import xarray as xr
from ..utils.prov import add_processing_level, echopype_prov_attrs, insert_input_processing_level
from .mvbs import get_MVBS_along_channels
def _set_var_attrs(da, long_name, units, round_digits, standard_name=None):
"""
Attach common attributes to DataArray variable.
Parameters
----------
da : xr.DataArray
DataArray that will receive attributes
long_name : str
Variable long_name attribute
units : str
Variable units attribute
round_digits : int
Number of digits after decimal point for rounding off actual_range
standard_name : str
CF standard_name, if available (optional)
"""
da.attrs = {
"long_name": long_name,
"units": units,
"actual_range": [
round(float(da.min().values), round_digits),
round(float(da.max().values), round_digits),
],
}
if standard_name:
da.attrs["standard_name"] = standard_name
def _set_MVBS_attrs(ds):
"""
Attach common attributes.
Parameters
----------
ds : xr.Dataset
dataset containing MVBS
"""
ds["ping_time"].attrs = {
"long_name": "Ping time",
"standard_name": "time",
"axis": "T",
}
_set_var_attrs(
ds["Sv"],
long_name="Mean volume backscattering strength (MVBS, mean Sv re 1 m-1)",
units="dB",
round_digits=2,
)
@add_processing_level("L3*")
def compute_MVBS(ds_Sv, range_meter_bin=20, ping_time_bin="20S"):
"""
Compute Mean Volume Backscattering Strength (MVBS)
based on intervals of range (``echo_range``) and ``ping_time`` specified in physical units.
Output of this function differs from that of ``compute_MVBS_index_binning``, which computes
bin-averaged Sv according to intervals of ``echo_range`` and ``ping_time`` specified as
index number.
Parameters
----------
ds_Sv : xr.Dataset
dataset containing Sv and ``echo_range`` [m]
range_meter_bin : Union[int, float]
bin size along ``echo_range`` in meters, default to ``20``
ping_time_bin : str
bin size along ``ping_time``, default to ``20S``
Returns
-------
A dataset containing bin-averaged Sv
"""
# create bin information for echo_range
range_interval = np.arange(0, ds_Sv["echo_range"].max() + range_meter_bin, range_meter_bin)
# create bin information needed for ping_time
ping_interval = (
ds_Sv.ping_time.resample(ping_time=ping_time_bin, skipna=True).asfreq().ping_time.values
)
# calculate the MVBS along each channel
MVBS_values = get_MVBS_along_channels(ds_Sv, range_interval, ping_interval)
# create MVBS dataset
ds_MVBS = xr.Dataset(
data_vars={"Sv": (["channel", "ping_time", "echo_range"], MVBS_values)},
coords={
"ping_time": ping_interval,
"channel": ds_Sv.channel,
"echo_range": range_interval[:-1],
},
)
# TODO: look into why 'filenames' exist here as a variable
# Added this check to support the test in test_process.py::test_compute_MVBS
if "filenames" in ds_MVBS.variables:
ds_MVBS = ds_MVBS.drop_vars("filenames")
# ping_time_bin parsing and conversions
# Need to convert between pd.Timedelta and np.timedelta64 offsets/frequency strings
# https://xarray.pydata.org/en/stable/generated/xarray.Dataset.resample.html
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.resample.html
# https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.html
# https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.resolution_string.html
# https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
# https://numpy.org/devdocs/reference/arrays.datetime.html#datetime-units
timedelta_units = {
"d": {"nptd64": "D", "unitstr": "day"},
"h": {"nptd64": "h", "unitstr": "hour"},
"t": {"nptd64": "m", "unitstr": "minute"},
"min": {"nptd64": "m", "unitstr": "minute"},
"s": {"nptd64": "s", "unitstr": "second"},
"l": {"nptd64": "ms", "unitstr": "millisecond"},
"ms": {"nptd64": "ms", "unitstr": "millisecond"},
"u": {"nptd64": "us", "unitstr": "microsecond"},
"us": {"nptd64": "ms", "unitstr": "millisecond"},
"n": {"nptd64": "ns", "unitstr": "nanosecond"},
"ns": {"nptd64": "ms", "unitstr": "millisecond"},
}
ping_time_bin_td = pd.Timedelta(ping_time_bin)
# res = resolution (most granular time unit)
ping_time_bin_resunit = ping_time_bin_td.resolution_string.lower()
ping_time_bin_resvalue = int(
ping_time_bin_td / np.timedelta64(1, timedelta_units[ping_time_bin_resunit]["nptd64"])
)
ping_time_bin_resunit_label = timedelta_units[ping_time_bin_resunit]["unitstr"]
# Attach attributes
_set_MVBS_attrs(ds_MVBS)
ds_MVBS["echo_range"].attrs = {"long_name": "Range distance", "units": "m"}
ds_MVBS["Sv"] = ds_MVBS["Sv"].assign_attrs(
{
"cell_methods": (
f"ping_time: mean (interval: {ping_time_bin_resvalue} {ping_time_bin_resunit_label} " # noqa
"comment: ping_time is the interval start) "
f"echo_range: mean (interval: {range_meter_bin} meter "
"comment: echo_range is the interval start)"
),
"binning_mode": "physical units",
"range_meter_interval": str(range_meter_bin) + "m",
"ping_time_interval": ping_time_bin,
"actual_range": [
round(float(ds_MVBS["Sv"].min().values), 2),
round(float(ds_MVBS["Sv"].max().values), 2),
],
}
)
prov_dict = echopype_prov_attrs(process_type="processing")
prov_dict["processing_function"] = "commongrid.compute_MVBS"
ds_MVBS = ds_MVBS.assign_attrs(prov_dict)
ds_MVBS["frequency_nominal"] = ds_Sv["frequency_nominal"] # re-attach frequency_nominal
ds_MVBS = insert_input_processing_level(ds_MVBS, input_ds=ds_Sv)
return ds_MVBS
@add_processing_level("L3*")
def compute_MVBS_index_binning(ds_Sv, range_sample_num=100, ping_num=100):
"""
Compute Mean Volume Backscattering Strength (MVBS)
based on intervals of ``range_sample`` and ping number (``ping_num``) specified in index number.
Output of this function differs from that of ``compute_MVBS``, which computes
bin-averaged Sv according to intervals of range (``echo_range``) and ``ping_time`` specified
in physical units.
Parameters
----------
ds_Sv : xr.Dataset
dataset containing ``Sv`` and ``echo_range`` [m]
range_sample_num : int
number of samples to average along the ``range_sample`` dimension, default to 100
ping_num : int
number of pings to average, default to 100
Returns
-------
A dataset containing bin-averaged Sv
"""
da_sv = 10 ** (ds_Sv["Sv"] / 10) # average should be done in linear domain
da = 10 * np.log10(
da_sv.coarsen(ping_time=ping_num, range_sample=range_sample_num, boundary="pad").mean(
skipna=True
)
)
# Attach attributes and coarsened echo_range
da.name = "Sv"
ds_MVBS = da.to_dataset()
ds_MVBS.coords["range_sample"] = (
"range_sample",
np.arange(ds_MVBS["range_sample"].size),
{"long_name": "Along-range sample number, base 0"},
) # reset range_sample to start from 0
ds_MVBS["echo_range"] = (
ds_Sv["echo_range"]
.coarsen( # binned echo_range (use first value in each average bin)
ping_time=ping_num, range_sample=range_sample_num, boundary="pad"
)
.min(skipna=True)
)
_set_MVBS_attrs(ds_MVBS)
ds_MVBS["Sv"] = ds_MVBS["Sv"].assign_attrs(
{
"cell_methods": (
f"ping_time: mean (interval: {ping_num} pings "
"comment: ping_time is the interval start) "
f"range_sample: mean (interval: {range_sample_num} samples along range "
"comment: range_sample is the interval start)"
),
"comment": "MVBS binned on the basis of range_sample and ping number specified as index numbers", # noqa
"binning_mode": "sample number",
"range_sample_interval": f"{range_sample_num} samples along range",
"ping_interval": f"{ping_num} pings",
"actual_range": [
round(float(ds_MVBS["Sv"].min().values), 2),
round(float(ds_MVBS["Sv"].max().values), 2),
],
}
)
prov_dict = echopype_prov_attrs(process_type="processing")
prov_dict["processing_function"] = "commongrid.compute_MVBS_index_binning"
ds_MVBS = ds_MVBS.assign_attrs(prov_dict)
ds_MVBS["frequency_nominal"] = ds_Sv["frequency_nominal"] # re-attach frequency_nominal
ds_MVBS = insert_input_processing_level(ds_MVBS, input_ds=ds_Sv)
return ds_MVBS
# def compute_NASC(
# ds_Sv: xr.Dataset,
# cell_dist: Union[int, float], # TODO: allow xr.DataArray
# cell_depth: Union[int, float], # TODO: allow xr.DataArray
# ) -> xr.Dataset:
# """
# Compute Nautical Areal Scattering Coefficient (NASC) from an Sv dataset.
# Parameters
# ----------
# ds_Sv : xr.Dataset
# A dataset containing Sv data.
# The Sv dataset must contain ``latitude``, ``longitude``, and ``depth`` as data variables.
# cell_dist: int, float
# The horizontal size of each NASC cell, in nautical miles [nmi]
# cell_depth: int, float
# The vertical size of each NASC cell, in meters [m]
# Returns
# -------
# xr.Dataset
# A dataset containing NASC
# Notes
# -----
# The NASC computation implemented here corresponds to the Echoview algorithm PRC_NASC
# https://support.echoview.com/WebHelp/Reference/Algorithms/Analysis_Variables/PRC_ABC_and_PRC_NASC.htm#PRC_NASC # noqa
# The difference is that since in echopype masking of the Sv dataset is done explicitly using
# functions in the ``mask`` subpackage so the computation only involves computing the
# mean Sv and the mean height within each cell.
# In addition, here the binning of pings into individual cells is based on the actual horizontal
# distance computed from the latitude and longitude coordinates of each ping in the Sv dataset.
# Therefore, both regular and irregular horizontal distance in the Sv dataset are allowed.
# This is different from Echoview's assumption of constant ping rate, vessel speed, and sample
# thickness when computing mean Sv.
# """
# # Check Sv contains lat/lon
# if "latitude" not in ds_Sv or "longitude" not in ds_Sv:
# raise ValueError("Both 'latitude' and 'longitude' must exist in the input Sv dataset.")
# # Check if depth vectors are identical within each channel
# if not ds_Sv["depth"].groupby("channel").map(check_identical_depth).all():
# raise ValueError(
# "Only Sv data with identical depth vectors across all pings "
# "are allowed in the current compute_NASC implementation."
# )
# # Get distance from lat/lon in nautical miles
# dist_nmi = get_distance_from_latlon(ds_Sv)
# # Find binning indices along distance
# bin_num_dist, dist_bin_idx = get_dist_bin_info(dist_nmi, cell_dist) # dist_bin_idx is 1-based
# # Find binning indices along depth: channel-dependent
# bin_num_depth, depth_bin_idx = get_depth_bin_info(ds_Sv, cell_depth) # depth_bin_idx is 1-based # noqa
# # Compute mean sv (volume backscattering coefficient, linear scale)
# # This is essentially to compute MVBS over a the cell defined here,
# # which are typically larger than those used for MVBS.
# # The implementation below is brute force looping, but can be optimized
# # by experimenting with different delayed schemes.
# # The optimized routines can then be used here and
# # in commongrid.compute_MVBS and clean.estimate_noise
# sv_mean = []
# for ch_seq in np.arange(ds_Sv["channel"].size):
# # TODO: .compute each channel sequentially?
# # dask.delay within each channel?
# ds_Sv_ch = ds_Sv["Sv"].isel(channel=ch_seq).data # preserve the underlying type
# sv_mean_dist_depth = []
# for dist_idx in np.arange(bin_num_dist) + 1: # along ping_time
# sv_mean_depth = []
# for depth_idx in np.arange(bin_num_depth) + 1: # along depth
# # Sv dim: ping_time x depth
# Sv_cut = ds_Sv_ch[dist_idx == dist_bin_idx, :][
# :, depth_idx == depth_bin_idx[ch_seq]
# ]
# sv_mean_depth.append(np.nanmean(10 ** (Sv_cut / 10)))
# sv_mean_dist_depth.append(sv_mean_depth)
# sv_mean.append(sv_mean_dist_depth)
# # Compute mean height
# # For data with uniform depth step size, mean height = vertical size of cell
# height_mean = cell_depth
# # TODO: generalize to variable depth step size
# ds_NASC = xr.DataArray(
# np.array(sv_mean) * height_mean,
# dims=["channel", "distance", "depth"],
# coords={
# "channel": ds_Sv["channel"].values,
# "distance": np.arange(bin_num_dist) * cell_dist,
# "depth": np.arange(bin_num_depth) * cell_depth,
# },
# name="NASC",
# ).to_dataset()
# ds_NASC["frequency_nominal"] = ds_Sv["frequency_nominal"] # re-attach frequency_nominal
# # Attach attributes
# _set_var_attrs(
# ds_NASC["NASC"],
# long_name="Nautical Areal Scattering Coefficient (NASC, m2 nmi-2)",
# units="m2 nmi-2",
# round_digits=3,
# )
# _set_var_attrs(ds_NASC["distance"], "Cumulative distance", "m", 3)
# _set_var_attrs(ds_NASC["depth"], "Cell depth", "m", 3, standard_name="depth")
# # Calculate and add ACDD bounding box global attributes
# ds_NASC.attrs["Conventions"] = "CF-1.7,ACDD-1.3"
# ds_NASC.attrs["time_coverage_start"] = np.datetime_as_string(
# ds_Sv["ping_time"].min().values, timezone="UTC"
# )
# ds_NASC.attrs["time_coverage_end"] = np.datetime_as_string(
# ds_Sv["ping_time"].max().values, timezone="UTC"
# )
# ds_NASC.attrs["geospatial_lat_min"] = round(float(ds_Sv["latitude"].min().values), 5)
# ds_NASC.attrs["geospatial_lat_max"] = round(float(ds_Sv["latitude"].max().values), 5)
# ds_NASC.attrs["geospatial_lon_min"] = round(float(ds_Sv["longitude"].min().values), 5)
# ds_NASC.attrs["geospatial_lon_max"] = round(float(ds_Sv["longitude"].max().values), 5)
# return ds_NASC
def regrid():
return 1
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"/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,805 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/echodata/test_echodata_combine.py | from datetime import datetime
from textwrap import dedent
from pathlib import Path
import tempfile
import numpy as np
import pytest
import xarray as xr
import echopype
from echopype.utils.coding import DEFAULT_ENCODINGS
from echopype.echodata import EchoData
from echopype.echodata.combine import (
_create_channel_selection_dict,
_check_channel_consistency,
_merge_attributes
)
@pytest.fixture
def ek60_diff_range_sample_test_data(test_path):
files = [
("ncei-wcsd", "SH1701", "TEST-D20170114-T202932.raw"),
("ncei-wcsd", "SH1701", "TEST-D20170114-T203337.raw"),
("ncei-wcsd", "SH1701", "TEST-D20170114-T203853.raw"),
]
return [test_path["EK60"].joinpath(*f) for f in files]
@pytest.fixture(scope="module")
def ek60_test_data(test_path):
files = [
("ncei-wcsd", "Summer2017-D20170620-T011027.raw"),
("ncei-wcsd", "Summer2017-D20170620-T014302.raw"),
("ncei-wcsd", "Summer2017-D20170620-T021537.raw"),
]
return [test_path["EK60"].joinpath(*f) for f in files]
@pytest.fixture(scope="module")
def ek60_multi_test_data(test_path):
files = [
("ncei-wcsd", "Summer2017-D20170620-T011027.raw"),
("ncei-wcsd", "Summer2017-D20170620-T014302.raw"),
("ncei-wcsd", "Summer2017-D20170620-T021537.raw"),
("ncei-wcsd", "Summer2017-D20170620-T024811.raw")
]
return [test_path["EK60"].joinpath(*f) for f in files]
@pytest.fixture
def ek80_test_data(test_path):
files = [
("echopype-test-D20211005-T000706.raw",),
("echopype-test-D20211005-T000737.raw",),
("echopype-test-D20211005-T000810.raw",),
("echopype-test-D20211005-T000843.raw",),
]
return [test_path["EK80_NEW"].joinpath(*f) for f in files]
@pytest.fixture
def ek80_broadband_same_range_sample_test_data(test_path):
files = [
("ncei-wcsd", "SH1707", "Reduced_D20170826-T205615.raw"),
("ncei-wcsd", "SH1707", "Reduced_D20170826-T205659.raw"),
("ncei-wcsd", "SH1707", "Reduced_D20170826-T205742.raw"),
]
return [test_path["EK80"].joinpath(*f) for f in files]
@pytest.fixture
def ek80_narrowband_diff_range_sample_test_data(test_path):
files = [
("ncei-wcsd", "SH2106", "EK80", "Reduced_Hake-D20210701-T130426.raw"),
("ncei-wcsd", "SH2106", "EK80", "Reduced_Hake-D20210701-T131325.raw"),
("ncei-wcsd", "SH2106", "EK80", "Reduced_Hake-D20210701-T131621.raw"),
]
return [test_path["EK80"].joinpath(*f) for f in files]
@pytest.fixture
def azfp_test_data(test_path):
# TODO: in the future we should replace these files with another set of
# similarly small set of files, for example the files from the location below:
# "https://rawdata.oceanobservatories.org/files/CE01ISSM/R00015/instrmts/dcl37/ZPLSC_sn55076/DATA/202109/*"
# This is because we have lost track of where the current files came from,
# since the filenames does not contain the site identifier.
files = [
("ooi", "18100407.01A"),
("ooi", "18100408.01A"),
("ooi", "18100409.01A"),
]
return [test_path["AZFP"].joinpath(*f) for f in files]
@pytest.fixture
def azfp_test_xml(test_path):
return test_path["AZFP"].joinpath(*("ooi", "18092920.XML"))
@pytest.fixture(
params=[
{
"sonar_model": "EK60",
"xml_file": None,
"files": "ek60_test_data"
},
{
"sonar_model": "EK60",
"xml_file": None,
"files": "ek60_diff_range_sample_test_data"
},
{
"sonar_model": "AZFP",
"xml_file": "azfp_test_xml",
"files": "azfp_test_data"
},
{
"sonar_model": "EK80",
"xml_file": None,
"files": "ek80_broadband_same_range_sample_test_data"
},
{
"sonar_model": "EK80",
"xml_file": None,
"files": "ek80_narrowband_diff_range_sample_test_data"
}
],
ids=["ek60", "ek60_diff_range_sample", "azfp",
"ek80_bb_same_range_sample", "ek80_nb_diff_range_sample"]
)
def raw_datasets(request):
files = request.param["files"]
xml_file = request.param["xml_file"]
if xml_file is not None:
xml_file = request.getfixturevalue(xml_file)
files = request.getfixturevalue(files)
return (
files,
request.param['sonar_model'],
xml_file,
request.node.callspec.id
)
def test_combine_echodata(raw_datasets):
(
files,
sonar_model,
xml_file,
param_id,
) = raw_datasets
eds = [echopype.open_raw(file, sonar_model, xml_file) for file in files]
append_dims = {"filenames", "time1", "time2", "time3", "ping_time"}
combined = echopype.combine_echodata(eds)
# Test Provenance conversion and combination attributes
for attr_token in ["software_name", "software_version", "time"]:
assert f"conversion_{attr_token}" in combined['Provenance'].attrs
assert f"combination_{attr_token}" in combined['Provenance'].attrs
def attr_time_to_dt(time_str):
return datetime.strptime(time_str, '%Y-%m-%dT%H:%M:%SZ')
assert (
attr_time_to_dt(combined['Provenance'].attrs['conversion_time']) <=
attr_time_to_dt(combined['Provenance'].attrs['combination_time'])
)
# get all possible dimensions that should be dropped
# these correspond to the attribute arrays created
all_drop_dims = []
for grp in combined.group_paths:
# format group name appropriately
ed_name = grp.replace("-", "_").replace("/", "_").lower()
# create and append attribute array dimension
all_drop_dims.append(ed_name + "_attr_key")
# add dimension for Provenance group
all_drop_dims.append("echodata_filename")
for group_name in combined.group_paths:
# get all Datasets to be combined
combined_group: xr.Dataset = combined[group_name]
eds_groups = [
ed[group_name]
for ed in eds
if ed[group_name] is not None
]
# all grp dimensions that are in all_drop_dims
if combined_group is None:
grp_drop_dims = []
concat_dims = []
else:
grp_drop_dims = list(set(combined_group.dims).intersection(set(all_drop_dims)))
concat_dims = list(set(combined_group.dims).intersection(append_dims))
# concat all Datasets along each concat dimension
diff_concats = []
for dim in concat_dims:
drop_dims = [c_dim for c_dim in concat_dims if c_dim != dim]
diff_concats.append(xr.concat([ed_subset.drop_dims(drop_dims) for ed_subset in eds_groups], dim=dim,
coords="minimal", data_vars="minimal"))
if len(diff_concats) < 1:
test_ds = eds_groups[0] # needed for groups that do not have append dims
else:
# create the full combined Dataset
test_ds = xr.merge(diff_concats, compat="override")
# correctly set filenames values for constructed combined Dataset
if "filenames" in test_ds:
test_ds.filenames.values[:] = np.arange(len(test_ds.filenames), dtype=int)
# correctly modify Provenance attributes, so we can do a direct compare
if group_name == "Provenance":
del test_ds.attrs["conversion_time"]
del combined_group.attrs["conversion_time"]
if group_name != "Provenance":
# TODO: Skip for Provenance group for now, need to figure out how to test this properly
if (combined_group is not None) and (test_ds is not None):
assert test_ds.identical(combined_group.drop_dims(grp_drop_dims))
def _check_prov_ds(prov_ds, eds):
"""Checks the Provenance dataset against source_filenames variable
and global attributes in the original echodata object"""
for i in range(prov_ds.dims["echodata_filename"]):
ed_ds = eds[i]
one_ds = prov_ds.isel(echodata_filename=i, filenames=i)
for key, value in one_ds.data_vars.items():
if key == "source_filenames":
ed_group = "Provenance"
assert np.array_equal(
ed_ds[ed_group][key].isel(filenames=0).values, value.values
)
else:
ed_group = value.attrs.get("echodata_group")
expected_val = ed_ds[ed_group].attrs[key]
if not isinstance(expected_val, str):
expected_val = str(expected_val)
assert str(value.values) == expected_val
@pytest.mark.parametrize("test_param", [
"single",
"multi",
"combined"
]
)
def test_combine_echodata_combined_append(ek60_multi_test_data, test_param, sonar_model="EK60"):
"""
Integration test for combine_echodata with the following cases:
- a single combined echodata object and a single echodata object
- a single combined echodata object and 2 single echodata objects
- a single combined echodata object and another combined single echodata object
"""
eds = [
echopype.open_raw(raw_file=file, sonar_model=sonar_model)
for file in ek60_multi_test_data
]
# create temporary directory for zarr store
temp_zarr_dir = tempfile.TemporaryDirectory()
first_zarr = (
temp_zarr_dir.name
+ f"/combined_echodata.zarr"
)
second_zarr = (
temp_zarr_dir.name
+ f"/combined_echodata2.zarr"
)
# First combined file
combined_ed = echopype.combine_echodata(eds[:2])
combined_ed.to_zarr(first_zarr, overwrite=True)
def _check_prov_ds_and_dims(sel_comb_ed, n_val_expected):
prov_ds = sel_comb_ed["Provenance"]
for _, n_val in prov_ds.dims.items():
assert n_val == n_val_expected
_check_prov_ds(prov_ds, eds)
# Checks for Provenance group
# Both dims of filenames and echodata filename should be 2
expected_n_vals = 2
_check_prov_ds_and_dims(combined_ed, expected_n_vals)
# Second combined file
combined_ed_other = echopype.combine_echodata(eds[2:])
combined_ed_other.to_zarr(second_zarr, overwrite=True)
combined_ed = echopype.open_converted(first_zarr)
combined_ed_other = echopype.open_converted(second_zarr)
# Set expected values for Provenance
if test_param == "single":
data_inputs = [combined_ed, eds[2]]
expected_n_vals = 3
elif test_param == "multi":
data_inputs = [combined_ed, eds[2], eds[3]]
expected_n_vals = 4
else:
data_inputs = [combined_ed, combined_ed_other]
expected_n_vals = 4
combined_ed2 = echopype.combine_echodata(data_inputs)
# Verify that combined objects are all EchoData objects
assert isinstance(combined_ed, EchoData)
assert isinstance(combined_ed_other, EchoData)
assert isinstance(combined_ed2, EchoData)
# Ensure that they're from the same file source
group_path = "Provenance"
for i in range(4):
ds_i = eds[i][group_path]
select_comb_ds = combined_ed[group_path] if i < 2 else combined_ed2[group_path]
if i < 3 or (i == 3 and test_param != "single"):
assert ds_i.source_filenames[0].values == select_comb_ds.source_filenames[i].values
# Check beam_group1. Should be exactly same xr dataset
group_path = "Sonar/Beam_group1"
for i in range(4):
ds_i = eds[i][group_path]
select_comb_ds = combined_ed[group_path] if i < 2 else combined_ed2[group_path]
if i < 3 or (i == 3 and test_param != "single"):
filt_ds_i = select_comb_ds.sel(ping_time=ds_i.ping_time)
assert filt_ds_i.identical(ds_i) is True
filt_combined = combined_ed2[group_path].sel(ping_time=combined_ed[group_path].ping_time)
assert filt_combined.identical(combined_ed[group_path]) is True
# Checks for Provenance group
# Both dims of filenames and echodata filename should be expected_n_vals
_check_prov_ds_and_dims(combined_ed2, expected_n_vals)
def test_combine_echodata_channel_selection():
"""
This test ensures that the ``channel_selection`` input
of ``combine_echodata`` is producing the correct output
for all sonar models except AD2CP.
"""
# TODO: Once a mock EchoData structure can be easily formed,
# we should implement this test.
pytest.skip("This test will not be implemented until after a mock EchoData object can be created.")
def test_attr_storage(ek60_test_data):
# check storage of attributes before combination in provenance group
eds = [echopype.open_raw(file, "EK60") for file in ek60_test_data]
combined = echopype.combine_echodata(eds)
for group, value in combined.group_map.items():
if value['ep_group'] is None:
group_path = 'Top-level'
else:
group_path = value['ep_group']
if f"{group}_attrs" in combined["Provenance"]:
group_attrs = combined["Provenance"][f"{group}_attrs"]
for i, ed in enumerate(eds):
for attr, value in ed[group_path].attrs.items():
assert str(
group_attrs.isel(echodata_filename=i)
.sel({f"{group}_attr_key": attr})
.values[()]
) == str(value)
# check selection by echodata_filename
for file in ek60_test_data:
assert Path(file).name in combined["Provenance"]["echodata_filename"]
for group in combined.group_map:
if f"{group}_attrs" in combined["Provenance"]:
group_attrs = combined["Provenance"][f"{group}_attrs"]
assert np.array_equal(
group_attrs.sel(
echodata_filename=Path(ek60_test_data[0]).name
),
group_attrs.isel(echodata_filename=0),
)
def test_combined_encodings(ek60_test_data):
eds = [echopype.open_raw(file, "EK60") for file in ek60_test_data]
combined = echopype.combine_echodata(eds)
encodings_to_drop = {'chunks', 'preferred_chunks', 'compressor', 'filters'}
group_checks = []
for _, value in combined.group_map.items():
if value['ep_group'] is None:
ds = combined['Top-level']
else:
ds = combined[value['ep_group']]
if ds is not None:
for k, v in ds.variables.items():
if k in DEFAULT_ENCODINGS:
encoding = ds[k].encoding
# remove any encoding relating to lazy loading
lazy_encodings = set(encoding.keys()).intersection(encodings_to_drop)
for encod_name in lazy_encodings:
del encoding[encod_name]
if encoding != DEFAULT_ENCODINGS[k]:
group_checks.append(
f" {value['name']}::{k}"
)
if len(group_checks) > 0:
all_messages = ['Encoding mismatch found!'] + group_checks
message_text = '\n'.join(all_messages)
raise AssertionError(message_text)
def test_combined_echodata_repr(ek60_test_data):
eds = [echopype.open_raw(file, "EK60") for file in ek60_test_data]
combined = echopype.combine_echodata(eds)
expected_repr = dedent(
f"""\
<EchoData: standardized raw data from Internal Memory>
Top-level: contains metadata about the SONAR-netCDF4 file format.
├── Environment: contains information relevant to acoustic propagation through water.
├── Platform: contains information about the platform on which the sonar is installed.
│ └── NMEA: contains information specific to the NMEA protocol.
├── Provenance: contains metadata about how the SONAR-netCDF4 version of the data were obtained.
├── Sonar: contains sonar system metadata and sonar beam groups.
│ └── Beam_group1: contains backscatter power (uncalibrated) and other beam or channel-specific data, including split-beam angle data when they exist.
└── Vendor_specific: contains vendor-specific information about the sonar and the data."""
)
assert isinstance(repr(combined), str) is True
actual = "\n".join(x.rstrip() for x in repr(combined).split("\n"))
assert actual == expected_repr
@pytest.mark.parametrize(
("all_chan_list", "channel_selection"),
[
(
[['a', 'b', 'c'], ['a', 'b', 'c']],
None
),
pytest.param(
[['a', 'b', 'c'], ['a', 'b']],
None,
marks=pytest.mark.xfail(strict=True,
reason="This test should not pass because the channels are not consistent")
),
(
[['a', 'b', 'c'], ['a', 'b', 'c']],
['a', 'b', 'c']
),
(
[['a', 'b', 'c'], ['a', 'b', 'c']],
['a', 'b']
),
(
[['a', 'b', 'c'], ['a', 'b']],
['a', 'b']
),
pytest.param(
[['a', 'c'], ['a', 'b', 'c']],
['a', 'b'],
marks=pytest.mark.xfail(strict=True,
reason="This test should not pass because we are selecting "
"channels that do not occur in each Dataset")
),
],
ids=["chan_sel_none_pass", "chan_sel_none_fail",
"chan_sel_same_as_given_chans", "chan_sel_subset_of_given_chans",
"chan_sel_subset_of_given_chans_uneven", "chan_sel_diff_from_some_given_chans"]
)
def test_check_channel_consistency(all_chan_list, channel_selection):
"""
Ensures that the channel consistency check for combine works
as expected using mock data.
"""
_check_channel_consistency(all_chan_list, "test_group", channel_selection)
# create duplicated dictionaries used within pytest parameterize
has_chan_dim_1_beam = {'Top-level': False, 'Environment': False, 'Platform': True,
'Platform/NMEA': False, 'Provenance': False, 'Sonar': True,
'Sonar/Beam_group1': True, 'Vendor_specific': True}
has_chan_dim_2_beam = {'Top-level': False, 'Environment': False, 'Platform': True,
'Platform/NMEA': False, 'Provenance': False, 'Sonar': True,
'Sonar/Beam_group1': True, 'Sonar/Beam_group2': True, 'Vendor_specific': True}
expected_1_beam_none = {'Top-level': None, 'Environment': None, 'Platform': None,
'Platform/NMEA': None, 'Provenance': None, 'Sonar': None,
'Sonar/Beam_group1': None, 'Vendor_specific': None}
expected_1_beam_a_b_sel = {'Top-level': None, 'Environment': None, 'Platform': ['a', 'b'],
'Platform/NMEA': None, 'Provenance': None, 'Sonar': ['a', 'b'],
'Sonar/Beam_group1': ['a', 'b'], 'Vendor_specific': ['a', 'b']}
@pytest.mark.parametrize(
("sonar_model", "has_chan_dim", "user_channel_selection", "expected_dict"),
[
(
["EK60", "ES70", "AZFP"],
has_chan_dim_1_beam,
[None],
expected_1_beam_none
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_1_beam,
[None],
expected_1_beam_none
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_2_beam,
[None],
{'Top-level': None, 'Environment': None, 'Platform': None, 'Platform/NMEA': None,
'Provenance': None, 'Sonar': None, 'Sonar/Beam_group1': None,
'Sonar/Beam_group2': None, 'Vendor_specific': None}
),
(
["EK60", "ES70", "AZFP"],
has_chan_dim_1_beam,
[['a', 'b'], {'Sonar/Beam_group1': ['a', 'b']}],
expected_1_beam_a_b_sel
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_1_beam,
[['a', 'b'], {'Sonar/Beam_group1': ['a', 'b']}],
expected_1_beam_a_b_sel
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_2_beam,
[['a', 'b']],
{'Top-level': None, 'Environment': None, 'Platform': ['a', 'b'], 'Platform/NMEA': None,
'Provenance': None, 'Sonar': ['a', 'b'], 'Sonar/Beam_group1': ['a', 'b'],
'Sonar/Beam_group2': ['a', 'b'], 'Vendor_specific': ['a', 'b']}
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_2_beam,
[{'Sonar/Beam_group1': ['a', 'b'], 'Sonar/Beam_group2': ['c', 'd']}],
{'Top-level': None, 'Environment': None, 'Platform': ['a', 'b', 'c', 'd'], 'Platform/NMEA': None,
'Provenance': None, 'Sonar': ['a', 'b', 'c', 'd'], 'Sonar/Beam_group1': ['a', 'b'],
'Sonar/Beam_group2': ['c', 'd'], 'Vendor_specific': ['a', 'b', 'c', 'd']}
),
(
["EK80", "ES80", "EA640"],
has_chan_dim_2_beam,
[{'Sonar/Beam_group1': ['a', 'b'], 'Sonar/Beam_group2': ['b', 'c', 'd']}],
{'Top-level': None, 'Environment': None, 'Platform': ['a', 'b', 'c', 'd'], 'Platform/NMEA': None,
'Provenance': None, 'Sonar': ['a', 'b', 'c', 'd'], 'Sonar/Beam_group1': ['a', 'b'],
'Sonar/Beam_group2': ['b', 'c', 'd'], 'Vendor_specific': ['a', 'b', 'c', 'd']}
),
],
ids=["EK60_no_sel", "EK80_no_sel_1_beam", "EK80_no_sel_2_beam", "EK60_chan_sel",
"EK80_chan_sel_1_beam", "EK80_list_chan_sel_2_beam", "EK80_dict_chan_sel_2_beam_diff_beam_group_chans",
"EK80_dict_chan_sel_2_beam_overlap_beam_group_chans"]
)
def test_create_channel_selection_dict(sonar_model, has_chan_dim,
user_channel_selection, expected_dict):
"""
Ensures that ``create_channel_selction_dict`` is constructing the correct output
for the sonar models ``EK60, EK80, AZFP`` and varying inputs for the input
``user_channel_selection``.
Notes
-----
The input ``has_chan_dim`` is unchanged except for the case where we are considering
an EK80 sonar model with two beam groups.
"""
for model in sonar_model:
for usr_sel_chan in user_channel_selection:
channel_selection_dict = _create_channel_selection_dict(model, has_chan_dim, usr_sel_chan)
assert channel_selection_dict == expected_dict
@pytest.mark.parametrize(
["attributes", "expected"],
[
([{"key1": ""}, {"key1": "test2"}, {"key1": "test1"}], {"key1": "test2"}),
(
[{"key1": "test1"}, {"key1": ""}, {"key1": "test2"}, {"key2": ""}],
{"key1": "test1", "key2": ""},
),
(
[
{"key1": ""},
{"key2": "test1", "key1": "test2"},
{"key2": "test3"},
],
{"key2": "test1", "key1": "test2"},
),
],
)
def test__merge_attributes(attributes, expected):
merged = _merge_attributes(attributes)
assert merged == expected
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,806 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/utils/test_utils_log.py | import pytest
import os.path
import platform
EXPECTED_MESSAGE = "Testing log function"
def logging_func(logger):
logger.info("Testing log function")
@pytest.fixture(params=[False, True])
def verbose(request):
return request.param
def test_init_logger():
import logging
from echopype.utils import log
logger = log._init_logger('echopype.testing0')
handlers = [h.name for h in logger.handlers]
assert isinstance(logger, logging.Logger) is True
assert logger.name == 'echopype.testing0'
assert len(logger.handlers) == 2
assert log.STDERR_NAME in handlers
assert log.STDOUT_NAME in handlers
def test_set_log_file():
from echopype.utils import log
logger = log._init_logger('echopype.testing1')
from tempfile import TemporaryDirectory
tmpdir = TemporaryDirectory()
tmpfile = os.path.join(tmpdir.name, "testfile.log")
log._set_logfile(logger, tmpfile)
handlers = [h.name for h in logger.handlers]
assert log.LOGFILE_HANDLE_NAME in handlers
# when done with temporary directory
# see: https://www.scivision.dev/python-tempfile-permission-error-windows/
try:
tmpdir.cleanup()
except Exception as e:
if platform.system() == "Windows":
pass
else:
raise e
def test_set_verbose(verbose, capsys):
from echopype.utils import log
logger = log._init_logger(f'echopype.testing_{str(verbose).lower()}')
# To pass through in caplog need to propagate
# logger.propagate = True
log._set_verbose(verbose)
logging_func(logger)
captured = capsys.readouterr()
if verbose:
assert EXPECTED_MESSAGE in captured.out
else:
assert "" in captured.out
def test_get_all_loggers():
import logging
from echopype.utils import log
all_loggers = log._get_all_loggers()
loggers = [logging.getLogger()] # get the root logger
loggers = loggers + [logging.getLogger(name) for name in logging.root.manager.loggerDict]
assert all_loggers == loggers
def run_verbose_test(logger, override, logfile, capsys):
import echopype as ep
import os
ep.verbose(logfile=logfile, override=override)
logging_func(logger)
captured = capsys.readouterr()
if override is True:
assert captured.out == ""
else:
assert EXPECTED_MESSAGE in captured.out
if logfile is not None:
assert os.path.exists(logfile)
with open(logfile) as f:
assert EXPECTED_MESSAGE in f.read()
@pytest.mark.parametrize(["id", "override", "logfile"], [
("fn", True, None),
("tn", False, None),
("tf", False, 'test.log')
])
def test_verbose(id, override, logfile, capsys):
from echopype.utils import log
logger = log._init_logger(f'echopype.testing_{id}')
if logfile is not None:
from tempfile import TemporaryDirectory
tmpdir = TemporaryDirectory()
tmpfile = os.path.join(tmpdir.name, logfile)
run_verbose_test(logger, override, tmpfile, capsys)
# when done with temporary directory
# see: https://www.scivision.dev/python-tempfile-permission-error-windows/
try:
tmpdir.cleanup()
except Exception as e:
if platform.system() == "Windows":
pass
else:
raise e
else:
run_verbose_test(logger, override, logfile, capsys)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,807 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/mask/__init__.py | from .api import apply_mask, frequency_differencing
__all__ = ["frequency_differencing", "apply_mask"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": 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"/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", 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"/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], 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"/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,808 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/convert/test_convert_ad2cp.py | """test_convert_ad2cp.py
This module test conversion of two sets of .ad2cp files.
Files under "normal" contain default data variables,
whereas files under "raw" additionally contain the IQ samples.
"""
import xarray as xr
import numpy as np
import netCDF4
import pytest
from tempfile import TemporaryDirectory
from pathlib import Path
from echopype import open_raw, open_converted
from echopype.testing import TEST_DATA_FOLDER
@pytest.fixture
def ocean_contour_export_dir(test_path):
return test_path["AD2CP"] / "ocean-contour"
@pytest.fixture
def ocean_contour_export_076_dir(ocean_contour_export_dir):
return ocean_contour_export_dir / "076"
@pytest.fixture
def ocean_contour_export_090_dir(ocean_contour_export_dir):
return ocean_contour_export_dir / "090"
@pytest.fixture
def output_dir():
return "/echopype_test-export"
def pytest_generate_tests(metafunc):
ad2cp_path = TEST_DATA_FOLDER / "ad2cp"
test_file_dir = (
ad2cp_path / "normal"
) # "normal" files do not have IQ samples
raw_test_file_dir = ad2cp_path / "raw" # "raw" files contain IQ samples
ad2cp_files = test_file_dir.glob("**/*.ad2cp")
raw_ad2cp_files = raw_test_file_dir.glob("**/*.ad2cp")
if "filepath" in metafunc.fixturenames:
metafunc.parametrize(
argnames="filepath",
argvalues=ad2cp_files,
ids=lambda f: str(f.name),
)
if "filepath_raw" in metafunc.fixturenames:
metafunc.parametrize(
argnames="filepath_raw",
argvalues=raw_ad2cp_files,
ids=lambda f: str(f.name),
)
@pytest.fixture
def filepath(request):
return request.param
@pytest.fixture
def filepath_raw(request):
return request.param
@pytest.fixture
def absolute_tolerance():
return 1e-6
def test_convert(filepath, output_dir):
with TemporaryDirectory() as tmpdir:
output_dir = Path(tmpdir + output_dir)
print("converting", filepath)
echodata = open_raw(raw_file=str(filepath), sonar_model="AD2CP")
echodata.to_netcdf(save_path=output_dir)
def test_convert_raw(
filepath_raw,
output_dir,
ocean_contour_export_090_dir,
ocean_contour_export_076_dir,
absolute_tolerance,
):
with TemporaryDirectory() as tmpdir:
output_dir = Path(tmpdir + output_dir)
print("converting raw", filepath_raw)
echodata = open_raw(raw_file=str(filepath_raw), sonar_model="AD2CP")
echodata.to_netcdf(save_path=output_dir)
_check_raw_output(
filepath_raw,
output_dir,
ocean_contour_export_090_dir,
ocean_contour_export_076_dir,
absolute_tolerance,
)
def _check_raw_output(
filepath_raw,
output_dir,
ocean_contour_export_090_dir,
ocean_contour_export_076_dir,
absolute_tolerance,
):
print("checking raw", filepath_raw)
echodata = open_converted(
converted_raw_path=output_dir.joinpath(
filepath_raw.with_suffix(".nc").name
)
)
if "090" in filepath_raw.parts:
ocean_contour_converted_config_path = (
ocean_contour_export_090_dir.joinpath(
filepath_raw.with_suffix(
filepath_raw.suffix + ".00000.nc"
).name
)
)
ocean_contour_converted_transmit_data_path = (
ocean_contour_converted_config_path
)
ocean_contour_converted_data_path = ocean_contour_converted_config_path
else:
ocean_contour_converted_config_path = (
ocean_contour_export_076_dir
/ filepath_raw.with_suffix("").name
/ "Raw Echo 1_1000 kHz_001.nc"
)
ocean_contour_converted_transmit_data_path = (
ocean_contour_export_076_dir
/ filepath_raw.with_suffix("").name
/ "Raw Echo 1_1000 kHz Tx_001.nc"
)
ocean_contour_converted_data_path = (
ocean_contour_export_076_dir
/ filepath_raw.with_suffix("").name
/ "Raw Echo 1_1000 kHz_001.nc"
)
if not all(
(
ocean_contour_converted_config_path.exists(),
ocean_contour_converted_transmit_data_path.exists(),
ocean_contour_converted_data_path.exists(),
)
):
pass
else:
# check pulse compression
base = xr.open_dataset(
str(ocean_contour_converted_config_path), group="Config"
)
pulse_compressed = 0
for i in range(1, 4):
if "090" in filepath_raw.parts:
if base.attrs[f"echo_pulseComp{i}"]:
pulse_compressed = i
break
else:
if base.attrs[f"Instrument_echo_pulseComp{i}"]:
pulse_compressed = i
break
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
if "pulse_compressed" in echodata[f"Sonar/Beam_group{i}"]:
pulse_compressed_vector = np.zeros(3)
pulse_compressed_vector[pulse_compressed - 1] = 1
assert (echodata[f"Sonar/Beam_group{i}"]["pulse_compressed"] == pulse_compressed_vector).all()
base.close()
# check raw data transmit samples
try:
netCDF4.Dataset(str(ocean_contour_converted_transmit_data_path))[
"Data/RawEcho1_1000kHzTx"
]
except IndexError:
# no transmit data in this dataset
pass
else:
base = xr.open_dataset(
str(ocean_contour_converted_transmit_data_path),
group="Data/RawEcho1_1000kHzTx",
)
if "090" in filepath_raw.parts:
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
if "transmit_pulse_r" in echodata[f"Sonar/Beam_group{i}"]:
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"][
"transmit_pulse_r"
].data.flatten(),
base["DataI"].data.flatten(),
atol=absolute_tolerance,
)
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"][
"transmit_pulse_i"
].data.flatten(),
base["DataQ"].data.flatten(),
atol=absolute_tolerance,
)
else:
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
if "transmit_pulse_r" in echodata[f"Sonar/Beam_group{i}"]:
# note the underscore
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"][
"transmit_pulse_r"
].data.flatten(),
base["Data_I"].data.flatten(),
atol=absolute_tolerance,
)
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"][
"transmit_pulse_i"
].data.flatten(),
base["Data_Q"].data.flatten(),
atol=absolute_tolerance,
)
base.close()
# check raw data samples
base = xr.open_dataset(
str(ocean_contour_converted_data_path),
group="Data/RawEcho1_1000kHz",
)
if "090" in filepath_raw.parts:
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
if "backscatter_r" in echodata[f"Sonar/Beam_group{i}"]:
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"]["backscatter_r"].data.flatten(),
base["DataI"].data.flatten(),
atol=absolute_tolerance,
)
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"]["backscatter_i"].data.flatten(),
base["DataQ"].data.flatten(),
atol=absolute_tolerance,
)
else:
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
if "transmit_pulse_r" in echodata[f"Sonar/Beam_group{i}"]:
# note the transpose
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"]["backscatter_r"].data.flatten(),
base["Data_I"].data.T.flatten(),
atol=absolute_tolerance,
)
assert np.allclose(
echodata[f"Sonar/Beam_group{i}"]["backscatter_i"].data.flatten(),
base["Data_Q"].data.T.flatten(),
atol=absolute_tolerance,
)
base.close()
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", 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"/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,809 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parsed_to_zarr_ek80.py | import numpy as np
import pandas as pd
import psutil
from .parsed_to_zarr_ek60 import Parsed2ZarrEK60
class Parsed2ZarrEK80(Parsed2ZarrEK60):
"""
Facilitates the writing of parsed data to
a zarr file for the EK80 sensor.
"""
def __init__(self, parser_obj):
super().__init__(parser_obj)
self.power_dims = ["timestamp", "channel_id"]
self.angle_dims = ["timestamp", "channel_id"]
self.complex_dims = ["timestamp", "channel_id"]
self.p2z_ch_ids = {} # channel ids for power, angle, complex
self.pow_ang_df = None # df that holds power and angle data
self.complex_df = None # df that holds complex data
# get channel and channel_id association and sort by channel_id
channels_old = list(self.parser_obj.config_datagram["configuration"].keys())
# sort the channels in ascending order
channels_new = channels_old[:]
channels_new.sort(reverse=False)
# obtain sort rule for the channel index
self.channel_sort_rule = {ch: channels_new.index(ch) for ch in channels_old}
def _get_num_transd_sec(self, x: pd.DataFrame):
"""
Returns the number of transducer sectors.
Parameters
----------
x : pd.DataFrame
DataFrame representing the complex series
"""
num_transducer_sectors = np.unique(
np.array(self.parser_obj.ping_data_dict["n_complex"][x.name[1]])
)
if num_transducer_sectors.size > 1: # this is not supposed to happen
raise ValueError("Transducer sector number changes in the middle of the file!")
else:
num_transducer_sectors = num_transducer_sectors[0]
return num_transducer_sectors
def _reshape_series(self, complex_series: pd.Series) -> pd.Series:
"""
Reshapes complex series into the correct form, taking
into account the beam dimension. The new shape of
each element of ``complex_series`` will be
(element length, num_transducer_sectors).
Parameters
----------
complex_series: pd.Series
Series representing the complex data
"""
# get dimension 2, which represents the number of transducer elements
dim_2 = pd.DataFrame(complex_series).apply(self._get_num_transd_sec, axis=1)
dim_2.name = "dim_2"
range_sample_len = complex_series.apply(
lambda x: x.shape[0] if isinstance(x, np.ndarray) else 0
)
# get dimension 1, which represents the new range_sample length
dim_1 = (range_sample_len / dim_2).astype("int")
dim_1.name = "dim_1"
comp_shape_df = pd.concat([complex_series, dim_1, dim_2], axis=1)
return comp_shape_df.apply(
lambda x: x.values[0].reshape((x.dim_1, x.dim_2))
if isinstance(x.values[0], np.ndarray)
else None,
axis=1,
)
@staticmethod
def _split_complex_data(complex_series: pd.Series) -> pd.DataFrame:
"""
Splits the 1D complex data into two 1D arrays
representing the real and imaginary parts of
the complex data, for each element in ``complex_series``.
Parameters
----------
complex_series : pd.Series
Series representing the complex data
Returns
-------
DataFrame with columns backscatter_r and
backscatter_i obtained from splitting the
complex data into real and imaginary parts,
respectively. The DataFrame will have the
same index as ``complex_series``.
"""
complex_split = complex_series.apply(
lambda x: [np.real(x), np.imag(x)] if isinstance(x, np.ndarray) else [None, None]
)
return pd.DataFrame(
data=complex_split.to_list(),
columns=["backscatter_r", "backscatter_i"],
index=complex_series.index,
)
def _write_complex(self, df: pd.DataFrame, max_mb: int):
"""
Writes the complex data and associated indices
to a zarr group.
Parameters
----------
df : pd.DataFrame
DataFrame that contains angle data
max_mb : int
Maximum MB allowed for each chunk
"""
# obtain complex data and drop NaNs
complex_series = df.set_index(self.complex_dims)["complex"].copy()
# get unique indices
times = complex_series.index.get_level_values(0).unique()
channels = complex_series.index.get_level_values(1).unique()
# sort the channels based on rule
_, indexer = channels.map(self.channel_sort_rule).sort_values(
ascending=True, return_indexer=True
)
channels = channels[indexer]
complex_series = self._reshape_series(complex_series)
complex_df = self._split_complex_data(complex_series)
self.p2z_ch_ids["complex"] = channels.values # store channel ids for variable
# create multi index using the product of the unique dims
unique_dims = [times, channels]
complex_df = self.set_multi_index(complex_df, unique_dims)
# write complex data to the complex group
zarr_grp = self.zarr_root.create_group("complex")
for column in complex_df:
self.write_df_column(
pd_series=complex_df[column],
zarr_grp=zarr_grp,
is_array=True,
unique_time_ind=times,
max_mb=max_mb,
)
# write the unique indices to the complex group
zarr_grp.array(
name=self.complex_dims[0], data=times.values, dtype=times.dtype.str, fill_value="NaT"
)
dtype = self._get_string_dtype(channels)
zarr_grp.array(
name=self.complex_dims[1], data=channels.values, dtype=dtype, fill_value=None
)
def _get_complex_size(self, df: pd.DataFrame) -> int:
"""
Returns the total memory in bytes required to
store the expanded complex data.
Parameters
----------
df: pd.DataFrame
DataFrame containing the complex and
the appropriate dimension data
"""
# get unique indices
times = df[self.complex_dims[0]].unique()
channels = df[self.complex_dims[1]].unique()
# get final form of index
multi_index = pd.MultiIndex.from_product([times, channels])
# get the total memory required for expanded zarr variables
complex_mem = self.array_series_bytes(df["complex"], multi_index.shape[0])
# multiply by 2 because we store both the complex and real parts
return 2 * complex_mem
def _get_zarr_dfs(self):
"""
Creates the DataFrames that hold the power, angle, and
complex data, which are needed for downstream computation.
"""
datagram_df = pd.DataFrame.from_dict(self.parser_obj.zarr_datagrams)
# get df corresponding to power and angle only
self.pow_ang_df = datagram_df[["power", "angle", "timestamp", "channel_id"]].copy()
# remove power and angle to conserve memory
del datagram_df["power"]
del datagram_df["angle"]
# drop rows with missing power and angle data
self.pow_ang_df.dropna(how="all", subset=["power", "angle"], inplace=True)
self.complex_df = datagram_df.dropna().copy()
def whether_write_to_zarr(self, mem_mult: float = 0.3) -> bool:
"""
Determines if the zarr data provided will expand
into a form that is larger than a percentage of
the total physical RAM.
Parameters
----------
mem_mult : float
Multiplier for total physical RAM
Notes
-----
If ``mem_mult`` times the total RAM is less
than the total memory required to store the
expanded zarr variables, this function will
return True, otherwise False.
"""
isinstance(self.datagram_df, pd.DataFrame)
# create zarr dfs, if they do not exist
if not isinstance(self.pow_ang_df, pd.DataFrame) and not isinstance(
self.complex_df, pd.DataFrame
):
self._get_zarr_dfs()
# get memory required for zarr data
pow_ang_total_mem = self._get_power_angle_size(self.pow_ang_df)
comp_total_mem = self._get_complex_size(self.complex_df)
total_mem = pow_ang_total_mem + comp_total_mem
# get statistics about system memory usage
mem = psutil.virtual_memory()
zarr_dgram_size = self._get_zarr_dgrams_size()
# approx. the amount of memory that will be used after expansion
req_mem = mem.used - zarr_dgram_size + total_mem
# free memory, if we no longer need it
if mem.total * mem_mult > req_mem:
del self.pow_ang_df
del self.complex_df
else:
del self.parser_obj.zarr_datagrams
return mem.total * mem_mult < req_mem
def datagram_to_zarr(self, max_mb: int) -> None:
"""
Facilitates the conversion of a list of
datagrams to a form that can be written
to a zarr store.
Parameters
----------
max_mb : int
Maximum MB allowed for each chunk
Notes
-----
This function specifically writes chunks along the time
index.
The chunking routine evenly distributes the times such
that each chunk differs by at most one time. This makes
it so that the memory required for each chunk is approximately
the same.
"""
self._create_zarr_info()
# create zarr dfs, if they do not exist
if not isinstance(self.pow_ang_df, pd.DataFrame) and not isinstance(
self.complex_df, pd.DataFrame
):
self._get_zarr_dfs()
del self.parser_obj.zarr_datagrams # free memory
if not self.pow_ang_df.empty:
self._write_power(df=self.pow_ang_df, max_mb=max_mb)
self._write_angle(df=self.pow_ang_df, max_mb=max_mb)
del self.pow_ang_df # free memory
if not self.complex_df.empty:
self._write_complex(df=self.complex_df, max_mb=max_mb)
del self.complex_df # free memory
self._close_store()
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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,810 | OSOceanAcoustics/echopype | refs/heads/main | /setup.py | from __future__ import absolute_import, division, print_function
from setuptools import setup
# Dynamically read dependencies from requirements file
with open("requirements.txt") as f:
requirements = f.readlines()
if __name__ == "__main__":
setup(install_requires=requirements)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,811 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/visualize/test_plot.py | import echopype
import echopype.visualize
from echopype.testing import TEST_DATA_FOLDER
from echopype.calibrate.calibrate_ek import CalibrateEK60
import pytest
from xarray.plot.facetgrid import FacetGrid
from matplotlib.collections import QuadMesh
import xarray as xr
import numpy as np
ek60_path = TEST_DATA_FOLDER / "ek60"
ek80_path = TEST_DATA_FOLDER / "ek80_new"
azfp_path = TEST_DATA_FOLDER / "azfp"
ad2cp_path = TEST_DATA_FOLDER / "ad2cp"
param_args = ("filepath", "sonar_model", "azfp_xml_path", "range_kwargs")
param_testdata = [
(
ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw",
"EK60",
None,
{},
),
(
ek60_path / "DY1002_EK60-D20100318-T023008_rep_freq.raw",
"EK60",
None,
{},
),
(
ek80_path / "echopype-test-D20211004-T235930.raw",
"EK80",
None,
{'waveform_mode': 'BB', 'encode_mode': 'complex'},
),
(
ek80_path / "D20211004-T233354.raw",
"EK80",
None,
{'waveform_mode': 'CW', 'encode_mode': 'power'},
),
(
ek80_path / "D20211004-T233115.raw",
"EK80",
None,
{'waveform_mode': 'CW', 'encode_mode': 'complex'},
),
(
azfp_path / "17082117.01A",
"AZFP",
azfp_path / "17041823.XML",
{},
), # Will always need env variables
pytest.param(
ad2cp_path / "raw" / "090" / "rawtest.090.00001.ad2cp",
"AD2CP",
None,
{},
marks=pytest.mark.xfail(
run=False,
reason="Not supported at the moment",
),
),
]
@pytest.mark.parametrize(param_args, param_testdata)
def test_plot_multi(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
):
# TODO: Need to figure out how to compare the actual rendered plots
ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
plots = echopype.visualize.create_echogram(ed)
assert isinstance(plots, list) is True
assert all(isinstance(plot, FacetGrid) for plot in plots) is True
@pytest.mark.parametrize(param_args, param_testdata)
def test_plot_single(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
):
# TODO: Need to figure out how to compare the actual rendered plots
ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
plots = echopype.visualize.create_echogram(
ed, channel=ed["Sonar/Beam_group1"].channel[0].values
)
assert isinstance(plots, list) is True
if (
sonar_model.lower() == 'ek80'
and range_kwargs['encode_mode'] == 'complex'
):
assert all(isinstance(plot, FacetGrid) for plot in plots) is True
else:
assert all(isinstance(plot, QuadMesh) for plot in plots) is True
@pytest.mark.parametrize(param_args, param_testdata)
def test_plot_multi_get_range(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
):
# TODO: Need to figure out how to compare the actual rendered plots
ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
if ed.sonar_model.lower() == 'azfp':
avg_temperature = ed["Environment"]['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
range_kwargs['env_params'] = env_params
plots = echopype.visualize.create_echogram(
ed, get_range=True, range_kwargs=range_kwargs
)
assert isinstance(plots, list) is True
assert all(isinstance(plot, FacetGrid) for plot in plots) is True
# Beam shape check
if (
sonar_model.lower() == 'ek80'
and range_kwargs['encode_mode'] == 'complex'
):
assert plots[0].axes.shape[-1] > 1
else:
assert plots[0].axes.shape[-1] == 1
# Channel shape check
assert ed["Sonar/Beam_group1"].channel.shape[0] == len(plots)
@pytest.mark.parametrize(param_args, param_testdata)
def test_plot_Sv(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
):
# TODO: Need to figure out how to compare the actual rendered plots
ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
if ed.sonar_model.lower() == 'azfp':
avg_temperature = ed["Environment"]['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
range_kwargs['env_params'] = env_params
if 'azfp_cal_type' in range_kwargs:
range_kwargs.pop('azfp_cal_type')
Sv = echopype.calibrate.compute_Sv(ed, **range_kwargs)
plots = echopype.visualize.create_echogram(Sv)
assert isinstance(plots, list) is True
assert all(isinstance(plot, FacetGrid) for plot in plots) is True
@pytest.mark.parametrize(param_args, param_testdata)
def test_plot_mvbs(
filepath,
sonar_model,
azfp_xml_path,
range_kwargs,
):
# TODO: Need to figure out how to compare the actual rendered plots
ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
if ed.sonar_model.lower() == 'azfp':
avg_temperature = ed["Environment"]['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
range_kwargs['env_params'] = env_params
if 'azfp_cal_type' in range_kwargs:
range_kwargs.pop('azfp_cal_type')
Sv = echopype.calibrate.compute_Sv(ed, **range_kwargs)
mvbs = echopype.commongrid.compute_MVBS(Sv, ping_time_bin='10S')
plots = []
try:
plots = echopype.visualize.create_echogram(mvbs)
except Exception as e:
assert isinstance(e, ValueError)
assert str(e) == "Ping time must have a length that is greater or equal to 2" # noqa
if len(plots) > 0:
assert all(isinstance(plot, FacetGrid) for plot in plots) is True
@pytest.mark.parametrize(
("vertical_offset", "expect_warning"),
[
(True, False),
([True], True),
(False, True),
(xr.DataArray(np.array(50.0)), False),
([10, 30.5], False),
(10, False),
(30.5, False),
],
)
def test_vertical_offset_echodata(vertical_offset, expect_warning, caplog):
from echopype.echodata import EchoData
from echopype.visualize.api import _add_vertical_offset
echopype.verbose()
filepath = ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw"
sonar_model = "EK60"
range_kwargs = {}
echodata = echopype.open_raw(
sonar_model=sonar_model, raw_file=filepath, xml_path=None
)
cal_obj = CalibrateEK60(
echodata=echodata,
env_params=range_kwargs.get("env_params", {}),
cal_params=None,
ecs_file=None,
)
range_in_meter = cal_obj.range_meter
single_array = range_in_meter.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11').isel(ping_time=0).values
no_input_vertical_offset = False
if isinstance(vertical_offset, list):
vertical_offset = vertical_offset[0]
echodata["Platform"] = echodata["Platform"].drop_vars('vertical_offset')
no_input_vertical_offset = True
if isinstance(vertical_offset, xr.DataArray):
original_array = single_array + vertical_offset.values
elif isinstance(vertical_offset, bool) and vertical_offset is True:
if not no_input_vertical_offset:
original_array = (
single_array
+ echodata["Platform"].vertical_offset.isel(time2=0).values
)
else:
original_array = single_array
elif vertical_offset is not False and isinstance(vertical_offset, (int, float)):
original_array = single_array + vertical_offset
else:
original_array = single_array
results = None
try:
results = _add_vertical_offset(
range_in_meter=range_in_meter,
vertical_offset=vertical_offset,
data_type=EchoData,
platform_data=echodata["Platform"],
)
if expect_warning:
assert 'WARNING' in caplog.text
except Exception as e:
assert isinstance(e, ValueError)
assert str(e) == 'vertical_offset must have any of these dimensions: ping_time, range_sample' # noqa
if isinstance(results, xr.DataArray):
final_array = results.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11').isel(ping_time=0).values
print(f"original_array = {original_array}")
print(f"results = {results}")
assert np.array_equal(original_array, final_array)
@pytest.mark.parametrize(
("vertical_offset", "expect_warning"),
[
(True, True),
(False, True),
(xr.DataArray(np.array(50.0)), False),
(10, False),
(30.5, False),
],
)
def test_vertical_offset_Sv_dataset(vertical_offset, expect_warning, caplog):
from echopype.visualize.api import _add_vertical_offset
echopype.verbose()
filepath = ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw"
sonar_model = "EK60"
range_kwargs = {}
echodata = echopype.open_raw(
sonar_model=sonar_model, raw_file=filepath, xml_path=None
)
Sv = echopype.calibrate.compute_Sv(echodata, **range_kwargs)
ds = Sv.set_coords('echo_range')
range_in_meter = ds.echo_range
single_array = range_in_meter.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11').isel(ping_time=0).values
if isinstance(vertical_offset, xr.DataArray):
original_array = single_array + vertical_offset.values
elif not isinstance(vertical_offset, bool) and isinstance(vertical_offset, (int, float)):
original_array = single_array + vertical_offset
else:
original_array = single_array
results = None
try:
results = _add_vertical_offset(
range_in_meter=range_in_meter,
vertical_offset=vertical_offset,
data_type=xr.Dataset,
)
if expect_warning:
assert 'WARNING' in caplog.text
except Exception as e:
assert isinstance(e, ValueError)
assert str(e) == 'vertical_offset must have any of these dimensions: ping_time, range_sample' # noqa
if isinstance(results, xr.DataArray):
final_array = results.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11').isel(ping_time=0).values
assert np.array_equal(original_array, final_array)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", 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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,812 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/env_params.py | import datetime
from typing import Dict, List, Literal, Optional, Union
import numpy as np
import xarray as xr
from ..echodata import EchoData
from ..utils import uwa
from .cal_params import param2da
ENV_PARAMS = (
"sound_speed",
"sound_absorption",
"temperature",
"salinity",
"pressure",
"pH",
"formula_sound_speed",
"formula_absorption",
)
def harmonize_env_param_time(
p: Union[int, float, xr.DataArray],
ping_time: Optional[Union[xr.DataArray, datetime.datetime]] = None,
):
"""
Harmonize time coordinate between Beam_groupX data and env_params to make sure
the timestamps are broadcast correctly in calibration and range calculations.
Regardless of the source, if `p` is an xr.DataArray, the time coordinate name
needs to be `time1` to be consistent with the time coordinate in EchoData["Environment"].
If `time1` is of length=1, the dimension `time1` is dropped.
Otherwise, `p` is interpolated to `ping_time`.
If `p` is not an xr.DataArray it is returned directly.
Parameters
----------
p
The environment parameter for timestamp check/correction
ping_time
Beam_groupX ping_time to interpolate env_params timestamps to.
Only used if p.time1 has length >1
Returns
-------
Environment parameter with correctly broadcasted timestamps
"""
if isinstance(p, xr.DataArray):
if "time1" not in p.coords:
return p
else:
# If there's only 1 time1 value,
# or if after dropping NaN there's only 1 time1 value
if p["time1"].size == 1 or p.dropna(dim="time1").size == 1:
return p.dropna(dim="time1").squeeze(dim="time1").drop("time1")
# Direct assignment if all timestamps are identical (EK60 data)
elif np.all(p["time1"].values == ping_time.values):
return p.rename({"time1": "ping_time"})
elif ping_time is None:
raise ValueError(f"ping_time needs to be provided for interpolating {p.name}")
else:
return p.dropna(dim="time1").interp(time1=ping_time)
else:
return p
def sanitize_user_env_dict(
user_dict: Dict[str, Union[int, float, List, xr.DataArray]],
channel: Union[List, xr.DataArray],
) -> Dict[str, Union[int, float, xr.DataArray]]:
"""
Creates a blueprint for ``env_params`` dictionary and
check the format/organize user-provided parameters.
This function is very similar to ``sanitize_user_cal_dict`` but much simpler,
without the interpolation routines needed for calibration parameters.
Parameters
----------
user_dict : dict
A dictionary containing user input calibration parameters
as {parameter name: parameter value}.
Parameter value has to be a scalar (int or float), a list or an ``xr.DataArray``.
If parameter value is an ``xr.DataArray``, it has to have a'channel' as a coordinate.
channel : list or xr.DataArray
A list of channels to be calibrated.
For EK80 data, this list has to corresponds with the subset of channels
selected based on waveform_mode and encode_mode
Returns
-------
dict
A dictionary containing sanitized user-provided environmental parameters.
Notes
-----
The user-provided 'sound_absorption' parameter has to be a list or an xr.DataArray,
because this parameter is frequency-dependen.
"""
# Make channel a sorted list
if not isinstance(channel, (list, xr.DataArray)):
raise ValueError("'channel' has to be a list or an xr.DataArray")
if isinstance(channel, xr.DataArray):
channel_sorted = sorted(channel.values)
else:
channel_sorted = sorted(channel)
# Screen parameters: only retain those defined in ENV_PARAMS
# -- transform params in list to xr.DataArray
# -- directly pass through params that are scalar or str
# -- check channel coordinate if params are xr.DataArray and pass it through
out_dict = dict.fromkeys(ENV_PARAMS)
for p_name, p_val in user_dict.items():
if p_name in out_dict:
# Param "sound_absorption" has to be an xr.DataArray or a list because it is freq-dep
if p_name == "sound_absorption" and not isinstance(p_val, (xr.DataArray, list)):
raise ValueError(
"The 'sound_absorption' parameter has to be a list or an xr.DataArray, "
"with 'channel' as an coordinate."
)
# If p_val an xr.DataArray, check existence and coordinates
if isinstance(p_val, xr.DataArray):
# if 'channel' is a coordinate, it has to match that of the data
if "channel" in p_val.coords:
if not (sorted(p_val.coords["channel"].values) == channel_sorted):
raise ValueError(
f"The 'channel' coordinate of {p_name} has to match "
"that of the data to be calibrated"
)
else:
raise ValueError(f"{p_name} has to have 'channel' as a coordinate")
out_dict[p_name] = p_val
# If p_val a scalar or str, do nothing
elif isinstance(p_val, (int, float, str)):
out_dict[p_name] = p_val
# If p_val a list, make it xr.DataArray
elif isinstance(p_val, list):
# check for list dimension happens within param2da()
out_dict[p_name] = param2da(p_val, channel)
# p_val has to be one of int, float, xr.DataArray
else:
raise ValueError(f"{p_name} has to be a scalar, list, or an xr.DataArray")
return out_dict
def get_env_params_AZFP(echodata: EchoData, user_dict: Optional[dict] = None):
"""Get env params using user inputs or values from data file.
Parameters
----------
echodata : EchoData
an echodata object containing the env params to be pulled from
user_dict : dict
user input dict containing env params
Returns
-------
dict
A dictionary containing the environmental parameters.
"""
# AZFP only has 1 beam group
beam = echodata["Sonar/Beam_group1"]
# Use sanitized user dict as blueprint
# out_dict contains only and all of the allowable cal params
out_dict = sanitize_user_env_dict(user_dict=user_dict, channel=beam["channel"])
out_dict.pop("pH") # AZFP formulae do not use pH
# For AZFP, salinity and pressure always come from user input
if ("salinity" not in out_dict) or ("pressure" not in out_dict):
raise ReferenceError("Please supply both salinity and pressure in env_params.")
# Needs to fill in temperature first before sound speed and absorption can be calculated
if out_dict["temperature"] is None:
out_dict["temperature"] = echodata["Environment"]["temperature"]
# Set sound speed and absorption formula source if not in user_dict
if out_dict["formula_sound_speed"] is None:
out_dict["formula_sound_speed"] = "AZFP"
if out_dict["formula_absorption"] is None:
out_dict["formula_absorption"] = "AZFP"
# Only fill in params that are None
for p, v in out_dict.items():
if v is None:
if p == "sound_speed":
out_dict[p] = uwa.calc_sound_speed(
temperature=out_dict["temperature"],
salinity=out_dict["salinity"],
pressure=out_dict["pressure"],
formula_source=out_dict["formula_sound_speed"],
)
elif p == "sound_absorption":
out_dict[p] = uwa.calc_absorption(
frequency=beam["frequency_nominal"],
temperature=out_dict["temperature"],
salinity=out_dict["salinity"],
pressure=out_dict["pressure"],
formula_source=out_dict["formula_absorption"],
)
# Harmonize time coordinate between Beam_groupX (ping_time) and env_params (time1)
# Note for AZFP data is always in Sonar/Beam_group1
for p in out_dict.keys():
out_dict[p] = harmonize_env_param_time(out_dict[p], ping_time=beam["ping_time"])
return out_dict
def get_env_params_EK(
sonar_type: Literal["EK60", "EK80"],
beam: xr.Dataset,
env: xr.Dataset,
user_dict: Optional[Dict] = None,
freq: xr.DataArray = None,
) -> Dict:
"""
Get env params using user inputs or values from data file.
Parameters
----------
sonar_type : str
Type of sonar, one of "EK60" or "EK80"
beam : xr.Dataset
A subset of Sonar/Beam_groupX that contains only the channels specified for calibration
env : xr.Dataset
A subset of Environment group that contains only the channels specified for calibration
user_dict : dict
User input dict containing env params
freq : xr.DataArray
Center frequency for the selected channels.
Required for EK80 calibration.
If provided for EK60 calibration,
it will be overwritten by the values in ``beam['frequency_nominal']``
Returns
-------
A dictionary containing the environmental parameters.
Notes
-----
EK60 file by default contains only sound speed and absorption.
In cases when temperature, salinity, and pressure values are supplied
by the user simultaneously, the sound speed and absorption are re-calculated.
EK80 file by default contains sound speed, temperature, depth, salinity, and acidity,
therefore absorption is always calculated unless it is supplied by the user.
In cases when temperature, salinity, and pressure values are supplied
by the user simultaneously, both the sound speed and absorption are re-calculated.
"""
if sonar_type not in ["EK60", "EK80"]:
raise ValueError("'sonar_type' has to be 'EK60' or 'EK80'")
# EK80 calibration requires freq, which is the channel center frequency
if sonar_type == "EK80":
if freq is None:
raise ValueError("'freq' is required for calibrating EK80-style data.")
else: # EK60
freq = beam["frequency_nominal"] # overwriting input if exists
# Use sanitized user dict as blueprint
# out_dict contains only and all of the allowable cal params
out_dict = sanitize_user_env_dict(user_dict=user_dict, channel=beam["channel"])
# Check absorption and sound speed formula
if out_dict["formula_absorption"] not in [None, "AM", "FG"]:
raise ValueError("'formula_absorption' has to be None, 'FG' or 'AM' for EK echosounders.")
if out_dict["formula_sound_speed"] not in (None, "Mackenzie"):
raise ValueError("'formula_absorption' has to be None or 'Mackenzie' for EK echosounders.")
# Calculation sound speed and absorption requires at least T, S, P
# tsp_all_exist controls wherher to calculate sound speed and absorption
tspa_all_exist = np.all(
[out_dict[p] is not None for p in ["temperature", "salinity", "pressure", "pH"]]
)
# If EK80, get env parameters from data if not provided in user dict
# All T, S, P, pH are needed because we always have to compute sound absorption for EK80 data
if not tspa_all_exist and sonar_type == "EK80":
for p_user, p_data in zip(
["temperature", "salinity", "pressure", "pH"], # name in defined env params
["temperature", "salinity", "depth", "acidity"], # name in EK80 data
):
out_dict[p_user] = user_dict.get(p_user, env[p_data])
# Sound speed
if out_dict["sound_speed"] is None:
if not tspa_all_exist:
# sounds speed always exist in EK60 and EK80 data
out_dict["sound_speed"] = env["sound_speed_indicative"]
out_dict.pop("formula_sound_speed")
else:
# default to Mackenzie sound speed formula if not in user dict
if out_dict["formula_sound_speed"] is None:
out_dict["formula_sound_speed"] = "Mackenzie"
out_dict["sound_speed"] = uwa.calc_sound_speed(
temperature=out_dict["temperature"],
salinity=out_dict["salinity"],
pressure=out_dict["pressure"],
formula_source=out_dict["formula_sound_speed"],
)
else:
out_dict.pop("formula_sound_speed") # remove this since no calculation
# Sound absorption
if out_dict["sound_absorption"] is None:
if not tspa_all_exist and sonar_type != "EK80": # this should not happen for EK80
# absorption always exist in EK60 data
out_dict["sound_absorption"] = env["absorption_indicative"]
out_dict.pop("formula_absorption")
else:
# default to FG absorption if not in user dict
if out_dict["formula_absorption"] is None:
out_dict["formula_absorption"] = "FG"
out_dict["sound_absorption"] = uwa.calc_absorption(
frequency=freq,
temperature=out_dict["temperature"],
salinity=out_dict["salinity"],
pressure=out_dict["pressure"],
pH=out_dict["pH"],
sound_speed=out_dict["sound_speed"],
formula_source=out_dict["formula_absorption"],
)
else:
out_dict.pop("formula_absorption") # remove this since no calculation
# Remove params if calculation for both sound speed and absorption didn't happen
if not ("formula_sound_speed" in out_dict or "formula_absorption" in out_dict):
[out_dict.pop(p) for p in ["temperature", "salinity", "pressure", "pH"]]
# Harmonize time coordinate between Beam_groupX (ping_time) and env_params (time1)
# Note for EK60 data is always in Sonar/Beam_group1
for p in out_dict.keys():
out_dict[p] = harmonize_env_param_time(out_dict[p], ping_time=beam["ping_time"])
return out_dict
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,813 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/utils/ek_raw_parsers.py | """
Code originally developed for pyEcholab
(https://github.com/CI-CMG/pyEcholab)
by Rick Towler <rick.towler@noaa.gov> at NOAA AFSC.
The code has been modified to handle split-beam data and
channel-transducer structure from different EK80 setups.
"""
import re
import struct
import sys
import xml.etree.ElementTree as ET
from collections import Counter
import numpy as np
from ...utils.log import _init_logger
from .ek_date_conversion import nt_to_unix
TCVR_CH_NUM_MATCHER = re.compile(r"\d{6}-\w{1,2}|\w{12}-\w{1,2}")
__all__ = [
"SimradNMEAParser",
"SimradDepthParser",
"SimradBottomParser",
"SimradAnnotationParser",
"SimradConfigParser",
"SimradRawParser",
]
logger = _init_logger(__name__)
class _SimradDatagramParser(object):
""""""
def __init__(self, header_type, header_formats):
self._id = header_type
self._headers = header_formats
self._versions = list(header_formats.keys())
def header_fmt(self, version=0):
return "=" + "".join([x[1] for x in self._headers[version]])
def header_size(self, version=0):
return struct.calcsize(self.header_fmt(version))
def header_fields(self, version=0):
return [x[0] for x in self._headers[version]]
def header(self, version=0):
return self._headers[version][:]
def validate_data_header(self, data):
if isinstance(data, dict):
type_ = data["type"][:3]
version = int(data["type"][3])
elif isinstance(data, str):
type_ = data[:3]
version = int(data[3])
else:
raise TypeError("Expected a dict or str")
if type_ != self._id:
raise ValueError("Expected data of type %s, not %s" % (self._id, type_))
if version not in self._versions:
raise ValueError("No parser available for type %s version %d" % (self._id, version))
return type_, version
def from_string(self, raw_string, bytes_read):
header = raw_string[:4]
if sys.version_info.major > 2:
header = header.decode()
id_, version = self.validate_data_header(header)
return self._unpack_contents(raw_string, bytes_read, version=version)
def to_string(self, data={}):
id_, version = self.validate_data_header(data)
datagram_content_str = self._pack_contents(data, version=version)
return self.finalize_datagram(datagram_content_str)
def _unpack_contents(self, raw_string="", version=0):
raise NotImplementedError
def _pack_contents(self, data={}, version=0):
raise NotImplementedError
@classmethod
def finalize_datagram(cls, datagram_content_str):
datagram_size = len(datagram_content_str)
final_fmt = "=l%dsl" % (datagram_size)
return struct.pack(final_fmt, datagram_size, datagram_content_str, datagram_size)
class SimradDepthParser(_SimradDatagramParser):
"""
ER60 Depth Detection datagram (from .bot files) contain the following keys:
type: string == 'DEP0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
transceiver_count: [long uint] with number of transceivers
depth: [float], one value for each active channel
reflectivity: [float], one value for each active channel
unused: [float], unused value for each active channel
The following methods are defined:
from_string(str): parse a raw ER60 Depth datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {
0: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("transceiver_count", "L"),
]
}
_SimradDatagramParser.__init__(self, "DEP", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
""""""
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 0:
data_fmt = "=3f"
data_size = struct.calcsize(data_fmt)
data["depth"] = np.zeros((data["transceiver_count"],))
data["reflectivity"] = np.zeros((data["transceiver_count"],))
data["unused"] = np.zeros((data["transceiver_count"],))
buf_indx = self.header_size(version)
for indx in range(data["transceiver_count"]):
d, r, u = struct.unpack(
data_fmt, raw_string[buf_indx : buf_indx + data_size] # noqa
)
data["depth"][indx] = d
data["reflectivity"][indx] = r
data["unused"][indx] = u
buf_indx += data_size
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
lengths = [
len(data["depth"]),
len(data["reflectivity"]),
len(data["unused"]),
data["transceiver_count"],
]
if len(set(lengths)) != 1:
min_indx = min(lengths)
logger.warning("Data lengths mismatched: d:%d, r:%d, u:%d, t:%d", *lengths)
logger.warning(" Using minimum value: %d", min_indx)
data["transceiver_count"] = min_indx
else:
min_indx = data["transceiver_count"]
for field in self.header_fields(version):
datagram_contents.append(data[field])
datagram_fmt += "%df" % (3 * data["transceiver_count"])
for indx in range(data["transceiver_count"]):
datagram_contents.extend(
[
data["depth"][indx],
data["reflectivity"][indx],
data["unused"][indx],
]
)
return struct.pack(datagram_fmt, *datagram_contents)
class SimradBottomParser(_SimradDatagramParser):
"""
Bottom Detection datagram contains the following keys:
type: string == 'BOT0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
datetime: datetime.datetime object of NT date converted to UTC
transceiver_count: long uint with number of transceivers
depth: [float], one value for each active channel
The following methods are defined:
from_string(str): parse a raw ER60 Bottom datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {
0: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("transceiver_count", "L"),
]
}
_SimradDatagramParser.__init__(self, "BOT", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
""""""
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 0:
depth_fmt = "=%dd" % (data["transceiver_count"],)
depth_size = struct.calcsize(depth_fmt)
buf_indx = self.header_size(version)
data["depth"] = np.fromiter(
struct.unpack(depth_fmt, raw_string[buf_indx : buf_indx + depth_size]), # noqa
"float",
)
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
if len(data["depth"]) != data["transceiver_count"]:
logger.warning(
"# of depth values %d does not match transceiver count %d",
len(data["depth"]),
data["transceiver_count"],
)
data["transceiver_count"] = len(data["depth"])
for field in self.header_fields(version):
datagram_contents.append(data[field])
datagram_fmt += "%dd" % (data["transceiver_count"])
datagram_contents.extend(data["depth"])
return struct.pack(datagram_fmt, *datagram_contents)
class SimradAnnotationParser(_SimradDatagramParser):
"""
ER60 Annotation datagram contains the following keys:
type: string == 'TAG0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
text: Annotation
The following methods are defined:
from_string(str): parse a raw ER60 Annotation datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {0: [("type", "4s"), ("low_date", "L"), ("high_date", "L")]}
_SimradDatagramParser.__init__(self, "TAG", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
""""""
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
# if version == 0:
# data['text'] = raw_string[self.header_size(version):].strip('\x00')
# if isinstance(data['text'], bytes):
# data['text'] = data['text'].decode()
if version == 0:
if sys.version_info.major > 2:
data["text"] = str(
raw_string[self.header_size(version) :].strip(b"\x00"),
"ascii",
errors="replace",
)
else:
data["text"] = unicode( # noqa
raw_string[self.header_size(version) :].strip("\x00"),
"ascii",
errors="replace",
)
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
for field in self.header_fields(version):
datagram_contents.append(data[field])
if data["text"][-1] != "\x00":
tmp_string = data["text"] + "\x00"
else:
tmp_string = data["text"]
# Pad with more nulls to 4-byte word boundary if necessary
if len(tmp_string) % 4:
tmp_string += "\x00" * (4 - (len(tmp_string) % 4))
datagram_fmt += "%ds" % (len(tmp_string))
datagram_contents.append(tmp_string)
return struct.pack(datagram_fmt, *datagram_contents)
class SimradNMEAParser(_SimradDatagramParser):
"""
ER60 NMEA datagram contains the following keys:
type: string == 'NME0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
nmea_string: full (original) NMEA string
The following methods are defined:
from_string(str): parse a raw ER60 NMEA datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
nmea_head_re = re.compile(r"\$[A-Za-z]{5},") # noqa
def __init__(self):
headers = {
0: [("type", "4s"), ("low_date", "L"), ("high_date", "L")],
1: [("type", "4s"), ("low_date", "L"), ("high_date", "L"), ("port", "32s")],
}
_SimradDatagramParser.__init__(self, "NME", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
"""
Parses the NMEA string provided in raw_string
:param raw_string: Raw NMEA string (i.e. '$GPZDA,160012.71,11,03,2004,-1,00*7D')
:type raw_string: str
:returns: None
"""
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
# Remove trailing \x00 from the PORT field for NME1, rest of the datagram identical to NME0
if version == 1:
data["port"] = data["port"].strip("\x00")
if version == 0 or version == 1:
if sys.version_info.major > 2:
data["nmea_string"] = str(
raw_string[self.header_size(version) :].strip(b"\x00"),
"ascii",
errors="replace",
)
else:
data["nmea_string"] = unicode( # noqa
raw_string[self.header_size(version) :].strip("\x00"),
"ascii",
errors="replace",
)
if self.nmea_head_re.match(data["nmea_string"][:7]) is not None:
data["nmea_talker"] = data["nmea_string"][1:3]
data["nmea_type"] = data["nmea_string"][3:6]
else:
data["nmea_talker"] = ""
data["nmea_type"] = "UNKNOWN"
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
for field in self.header_fields(version):
datagram_contents.append(data[field])
if data["nmea_string"][-1] != "\x00":
tmp_string = data["nmea_string"] + "\x00"
else:
tmp_string = data["nmea_string"]
# Pad with more nulls to 4-byte word boundary if necessary
if len(tmp_string) % 4:
tmp_string += "\x00" * (4 - (len(tmp_string) % 4))
datagram_fmt += "%ds" % (len(tmp_string))
# Convert to python string if needed
if isinstance(tmp_string, str):
tmp_string = tmp_string.encode("ascii", errors="replace")
datagram_contents.append(tmp_string)
return struct.pack(datagram_fmt, *datagram_contents)
class SimradMRUParser(_SimradDatagramParser):
"""
EK80 MRU datagram contains the following keys:
type: string == 'MRU0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
heave: float
roll : float
pitch: float
heading: float
The following methods are defined:
from_string(str): parse a raw ER60 NMEA datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {
0: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("heave", "f"),
("roll", "f"),
("pitch", "f"),
("heading", "f"),
]
}
_SimradDatagramParser.__init__(self, "MRU", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
"""
Unpacks the data in raw_string into dictionary containing MRU data
:param raw_string:
:type raw_string: str
:returns: None
"""
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
for field in self.header_fields(version):
datagram_contents.append(data[field])
if data["nmea_string"][-1] != "\x00":
tmp_string = data["nmea_string"] + "\x00"
else:
tmp_string = data["nmea_string"]
# Pad with more nulls to 4-byte word boundary if necessary
if len(tmp_string) % 4:
tmp_string += "\x00" * (4 - (len(tmp_string) % 4))
datagram_fmt += "%ds" % (len(tmp_string))
# Convert to python string if needed
if isinstance(tmp_string, str):
tmp_string = tmp_string.encode("ascii", errors="replace")
datagram_contents.append(tmp_string)
return struct.pack(datagram_fmt, *datagram_contents)
class SimradXMLParser(_SimradDatagramParser):
"""
EK80 XML datagram contains the following keys:
type: string == 'XML0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
subtype: string representing Simrad XML datagram type:
configuration, environment, or parameter
[subtype]: dict containing the data specific to the XML subtype.
The following methods are defined:
from_string(str): parse a raw EK80 XML datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
# define the XML parsing options - here we define dictionaries for various xml datagram
# types. When parsing that xml datagram, these dictionaries are used to inform the parser about
# type conversion, name wrangling, and delimiter. If a field is missing, the parser
# assumes no conversion: type will be string, default mangling, and that there is only 1
# element.
#
# the dicts are in the form:
# 'XMLParamName':[converted type,'fieldname', 'parse char']
#
# For example: 'PulseDurationFM':[float,'pulse_duration_fm',';']
#
# will result in a return dictionary field named 'pulse_duration_fm' that contains a list
# of float values parsed from a string that uses ';' to separate values. Empty strings
# for fieldname and/or parse char result in the default action for those parsing steps.
channel_parsing_options = {
"MaxTxPowerTransceiver": [int, "", ""],
"PulseDuration": [float, "", ";"],
"PulseDurationFM": [float, "pulse_duration_fm", ";"],
"SampleInterval": [float, "", ";"],
"ChannelID": [str, "channel_id", ""],
"HWChannelConfiguration": [str, "hw_channel_configuration", ""],
}
transceiver_parsing_options = {
"TransceiverNumber": [int, "", ""],
"Version": [str, "transceiver_version", ""],
"IPAddress": [str, "ip_address", ""],
"Impedance": [int, "", ""],
}
transducer_parsing_options = {
"SerialNumber": [str, "transducer_serial_number", ""],
"Frequency": [float, "transducer_frequency", ""],
"FrequencyMinimum": [float, "transducer_frequency_minimum", ""],
"FrequencyMaximum": [float, "transducer_frequency_maximum", ""],
"BeamType": [int, "transducer_beam_type", ""],
"Gain": [float, "", ";"],
"SaCorrection": [float, "", ";"],
"MaxTxPowerTransducer": [float, "", ""],
"EquivalentBeamAngle": [float, "", ""],
"BeamWidthAlongship": [float, "", ""],
"BeamWidthAthwartship": [float, "", ""],
"AngleSensitivityAlongship": [float, "", ""],
"AngleSensitivityAthwartship": [float, "", ""],
"AngleOffsetAlongship": [float, "", ""],
"AngleOffsetAthwartship": [float, "", ""],
"DirectivityDropAt2XBeamWidth": [
float,
"directivity_drop_at_2x_beam_width",
"",
],
"TransducerOffsetX": [float, "", ""],
"TransducerOffsetY": [float, "", ""],
"TransducerOffsetZ": [float, "", ""],
"TransducerAlphaX": [float, "", ""],
"TransducerAlphaY": [float, "", ""],
"TransducerAlphaZ": [float, "", ""],
}
header_parsing_options = {"Version": [str, "application_version", ""]}
envxdcr_parsing_options = {"SoundSpeed": [float, "transducer_sound_speed", ""]}
environment_parsing_options = {
"Depth": [float, "", ""],
"Acidity": [float, "", ""],
"Salinity": [float, "", ""],
"SoundSpeed": [float, "", ""],
"Temperature": [float, "", ""],
"Latitude": [float, "", ""],
"SoundVelocityProfile": [float, "", ";"],
"DropKeelOffset": [float, "", ""],
"DropKeelOffsetIsManual": [int, "", ""],
"WaterLevelDraft": [float, "", ""],
"WaterLevelDraftIsManual": [int, "", ""],
}
parameter_parsing_options = {
"ChannelID": [str, "channel_id", ""],
"ChannelMode": [int, "", ""],
"PulseForm": [int, "", ""],
"Frequency": [float, "", ""],
"PulseDuration": [float, "", ""],
"SampleInterval": [float, "", ""],
"TransmitPower": [float, "", ""],
"Slope": [float, "", ""],
}
def __init__(self):
headers = {0: [("type", "4s"), ("low_date", "L"), ("high_date", "L")]}
_SimradDatagramParser.__init__(self, "XML", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
"""
Parses the NMEA string provided in raw_string
:param raw_string: Raw NMEA string (i.e. '$GPZDA,160012.71,11,03,2004,-1,00*7D')
:type raw_string: str
:returns: None
"""
def from_CamelCase(xml_param):
"""
convert name from CamelCase to fit with existing naming convention by
inserting an underscore before each capital and then lowering the caps
e.g. CamelCase becomes camel_case.
"""
idx = list(reversed([i for i, c in enumerate(xml_param) if c.isupper()]))
param_len = len(xml_param)
for i in idx:
# check if we should insert an underscore
if i > 0 and i < param_len:
xml_param = xml_param[:i] + "_" + xml_param[i:]
xml_param = xml_param.lower()
return xml_param
def dict_to_dict(xml_dict, data_dict, parse_opts):
"""
dict_to_dict appends the ETree xml value dicts to a provided dictionary
and along the way converts the key name to conform to the project's
naming convention and optionally parses and or converts values as
specified in the parse_opts dictionary.
"""
for k in xml_dict:
# check if we're parsing this key/value
if k in parse_opts:
# try to parse the string
if parse_opts[k][2]:
try:
data = xml_dict[k].split(parse_opts[k][2])
except:
# bad or empty parse character(s) provided
data = xml_dict[k]
else:
# no parse char provided - nothing to parse
data = xml_dict[k]
# try to convert to specified type
if isinstance(data, list):
for i in range(len(data)):
try:
data[i] = parse_opts[k][0](data[i])
except:
pass
else:
data = parse_opts[k][0](data)
# and add the value to the provided dict
if parse_opts[k][1]:
# add using the specified key name
data_dict[parse_opts[k][1]] = data
else:
# add using the default key name wrangling
data_dict[from_CamelCase(k)] = data
else:
# nothing to do with the value string
data = xml_dict[k]
# add the parameter to the provided dictionary
data_dict[from_CamelCase(k)] = data
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 0:
if sys.version_info.major > 2:
xml_string = str(
raw_string[self.header_size(version) :].strip(b"\x00"),
"ascii",
errors="replace",
)
else:
xml_string = unicode( # noqa
raw_string[self.header_size(version) :].strip("\x00"),
"ascii",
errors="replace",
)
# get the ElementTree element
root = ET.fromstring(xml_string)
# get the XML message type
data["subtype"] = root.tag.lower()
# create the dictionary that contains the message data
data[data["subtype"]] = {}
# parse it
if data["subtype"] == "configuration":
# parse the Transceiver section
for tcvr in root.iter("Transceiver"):
# parse the Transceiver section
tcvr_xml = tcvr.attrib
# parse the Channel section -- this works with multiple channels
# under 1 transceiver
for tcvr_ch in tcvr.iter("Channel"):
tcvr_ch_xml = tcvr_ch.attrib
channel_id = tcvr_ch_xml["ChannelID"]
# create the configuration dict for this channel
data["configuration"][channel_id] = {}
# add the transceiver data to the config dict (this is
# replicated for all channels)
dict_to_dict(
tcvr_xml,
data["configuration"][channel_id],
self.transceiver_parsing_options,
)
# add the general channel data to the config dict
dict_to_dict(
tcvr_ch_xml,
data["configuration"][channel_id],
self.channel_parsing_options,
)
# check if there are >1 transducer under a single transceiver channel
if len(list(tcvr_ch)) > 1:
ValueError("Found >1 transducer under a single transceiver channel!")
else: # should only have 1 transducer
tcvr_ch_xducer = tcvr_ch.find(
"Transducer"
) # get Element of this xducer
f_par = tcvr_ch_xducer.findall("FrequencyPar")
# Save calibration parameters
if f_par:
cal_par = {
"frequency": np.array(
[int(f.attrib["Frequency"]) for f in f_par]
),
"gain": np.array([float(f.attrib["Gain"]) for f in f_par]),
"impedance": np.array(
[float(f.attrib["Impedance"]) for f in f_par]
),
"phase": np.array([float(f.attrib["Phase"]) for f in f_par]),
"beamwidth_alongship": np.array(
[float(f.attrib["BeamWidthAlongship"]) for f in f_par]
),
"beamwidth_athwartship": np.array(
[float(f.attrib["BeamWidthAthwartship"]) for f in f_par]
),
"angle_offset_alongship": np.array(
[float(f.attrib["AngleOffsetAlongship"]) for f in f_par]
),
"angle_offset_athwartship": np.array(
[float(f.attrib["AngleOffsetAthwartship"]) for f in f_par]
),
}
data["configuration"][channel_id]["calibration"] = cal_par
# add the transducer data to the config dict
dict_to_dict(
tcvr_ch_xducer.attrib,
data["configuration"][channel_id],
self.transducer_parsing_options,
)
# get unique transceiver channel number stored in channel_id
tcvr_ch_num = TCVR_CH_NUM_MATCHER.search(channel_id)[0]
# parse the Transducers section from the root
# TODO Remove Transducers if doesn't exist
xducer = root.find("Transducers")
if xducer is not None:
# built occurrence lookup table for transducer name
xducer_name_list = []
for xducer_ch in xducer.iter("Transducer"):
xducer_name_list.append(xducer_ch.attrib["TransducerName"])
# find matching transducer for this channel_id
match_found = False
for xducer_ch in xducer.iter("Transducer"):
if not match_found:
xducer_ch_xml = xducer_ch.attrib
match_name = (
xducer_ch.attrib["TransducerName"]
== tcvr_ch_xducer.attrib["TransducerName"]
)
if xducer_ch.attrib["TransducerSerialNumber"] == "":
match_sn = False
else:
match_sn = (
xducer_ch.attrib["TransducerSerialNumber"]
== tcvr_ch_xducer.attrib["SerialNumber"]
)
match_tcvr = (
tcvr_ch_num in xducer_ch.attrib["TransducerCustomName"]
)
# if find match add the transducer mounting details
if (
Counter(xducer_name_list)[
xducer_ch.attrib["TransducerName"]
]
> 1
):
# if more than one transducer has the same name
# only check sn and transceiver unique number
match_found = match_sn or match_tcvr
else:
match_found = match_name or match_sn or match_tcvr
# add transducer mounting details
if match_found:
dict_to_dict(
xducer_ch_xml,
data["configuration"][channel_id],
self.transducer_parsing_options,
)
# add the header data to the config dict
h = root.find("Header")
dict_to_dict(
h.attrib,
data["configuration"][channel_id],
self.header_parsing_options,
)
elif data["subtype"] == "parameter":
# parse the parameter XML datagram
for h in root.iter("Channel"):
parm_xml = h.attrib
# add the data to the environment dict
dict_to_dict(parm_xml, data["parameter"], self.parameter_parsing_options)
elif data["subtype"] == "environment":
# parse the environment XML datagram
for h in root.iter("Environment"):
env_xml = h.attrib
# add the data to the environment dict
dict_to_dict(env_xml, data["environment"], self.environment_parsing_options)
for h in root.iter("Transducer"):
transducer_xml = h.attrib
# add the data to the environment dict
dict_to_dict(
transducer_xml,
data["environment"],
self.envxdcr_parsing_options,
)
data["xml"] = xml_string
return data
def _pack_contents(self, data, version):
def to_CamelCase(xml_param):
"""
convert name from project's convention to CamelCase for converting back to
XML to in Kongsberg's convention.
"""
idx = list(reversed([i for i, c in enumerate(xml_param) if c.isupper()]))
param_len = len(xml_param)
for i in idx:
# check if we should insert an underscore
if idx > 0 and idx < param_len - 1:
xml_param = xml_param[:idx] + "_" + xml_param[idx:]
xml_param = xml_param.lower()
return xml_param
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
for field in self.header_fields(version):
datagram_contents.append(data[field])
if data["nmea_string"][-1] != "\x00":
tmp_string = data["nmea_string"] + "\x00"
else:
tmp_string = data["nmea_string"]
# Pad with more nulls to 4-byte word boundary if necessary
if len(tmp_string) % 4:
tmp_string += "\x00" * (4 - (len(tmp_string) % 4))
datagram_fmt += "%ds" % (len(tmp_string))
# Convert to python string if needed
if isinstance(tmp_string, str):
tmp_string = tmp_string.encode("ascii", errors="replace")
datagram_contents.append(tmp_string)
return struct.pack(datagram_fmt, *datagram_contents)
class SimradFILParser(_SimradDatagramParser):
"""
EK80 FIL datagram contains the following keys:
type: string == 'FIL1'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
stage: int
channel_id: string
n_coefficients: int
decimation_factor: int
coefficients: np.complex64
The following methods are defined:
from_string(str): parse a raw EK80 FIL datagram
(with leading/trailing datagram size stripped)
to_string(): Returns the datagram as a raw string
(including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {
1: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("stage", "h"),
("spare", "2s"),
("channel_id", "128s"),
("n_coefficients", "h"),
("decimation_factor", "h"),
]
}
_SimradDatagramParser.__init__(self, "FIL", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
data = {}
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
# handle Python 3 strings
if (sys.version_info.major > 2) and isinstance(data[field], bytes):
data[field] = data[field].decode("latin_1")
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 1:
# clean up the channel ID
data["channel_id"] = data["channel_id"].strip("\x00")
# unpack the coefficients
indx = self.header_size(version)
block_size = data["n_coefficients"] * 8
data["coefficients"] = np.frombuffer(
raw_string[indx : indx + block_size], dtype="complex64" # noqa
)
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
pass
elif version == 1:
for field in self.header_fields(version):
datagram_contents.append(data[field])
datagram_fmt += "%ds" % (len(data["beam_config"]))
datagram_contents.append(data["beam_config"])
return struct.pack(datagram_fmt, *datagram_contents)
class SimradConfigParser(_SimradDatagramParser):
"""
Simrad Configuration Datagram parser operates on dictionaries with the following keys:
type: string == 'CON0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
survey_name [str]
transect_name [str]
sounder_name [str]
version [str]
spare0 [str]
transceiver_count [long]
transceivers [list] List of dicts representing Transducer Configs:
ME70 Data contains the following additional values (data contained w/in first 14
bytes of the spare0 field)
multiplexing [short] Always 0
time_bias [long] difference between UTC and local time in min.
sound_velocity_avg [float] [m/s]
sound_velocity_transducer [float] [m/s]
beam_config [str] Raw XML string containing beam config. info
Transducer Config Keys (ER60/ES60/ES70 sounders):
channel_id [str] channel ident string
beam_type [long] Type of channel (0 = Single, 1 = Split)
frequency [float] channel frequency
equivalent_beam_angle [float] dB
beamwidth_alongship [float]
beamwidth_athwartship [float]
angle_sensitivity_alongship [float]
angle_sensitivity_athwartship [float]
angle_offset_alongship [float]
angle_offset_athwartship [float]
pos_x [float]
pos_y [float]
pos_z [float]
dir_x [float]
dir_y [float]
dir_z [float]
pulse_length_table [float[5]]
spare1 [str]
gain_table [float[5]]
spare2 [str]
sa_correction_table [float[5]]
spare3 [str]
gpt_software_version [str]
spare4 [str]
Transducer Config Keys (ME70 sounders):
channel_id [str] channel ident string
beam_type [long] Type of channel (0 = Single, 1 = Split)
reserved1 [float] channel frequency
equivalent_beam_angle [float] dB
beamwidth_alongship [float]
beamwidth_athwartship [float]
angle_sensitivity_alongship [float]
angle_sensitivity_athwartship [float]
angle_offset_alongship [float]
angle_offset_athwartship [float]
pos_x [float]
pos_y [float]
pos_z [float]
beam_steering_angle_alongship [float]
beam_steering_angle_athwartship [float]
beam_steering_angle_unused [float]
pulse_length [float]
reserved2 [float]
spare1 [str]
gain [float]
reserved3 [float]
spare2 [str]
sa_correction [float]
reserved4 [float]
spare3 [str]
gpt_software_version [str]
spare4 [str]
from_string(str): parse a raw config datagram
(with leading/trailing datagram size stripped)
to_string(dict): Returns raw string (including leading/trailing size fields)
ready for writing to disk
"""
COMMON_KEYS = [
("channel_id", "128s"),
("beam_type", "l"),
("frequency", "f"),
("gain", "f"),
("equivalent_beam_angle", "f"),
("beamwidth_alongship", "f"),
("beamwidth_athwartship", "f"),
("angle_sensitivity_alongship", "f"),
("angle_sensitivity_athwartship", "f"),
("angle_offset_alongship", "f"),
("angle_offset_athwartship", "f"),
("pos_x", "f"),
("pos_y", "f"),
("pos_z", "f"),
("dir_x", "f"),
("dir_y", "f"),
("dir_z", "f"),
("pulse_length_table", "5f"),
("spare1", "8s"),
("gain_table", "5f"),
("spare2", "8s"),
("sa_correction_table", "5f"),
("spare3", "8s"),
("gpt_software_version", "16s"),
("spare4", "28s"),
]
def __init__(self):
headers = {
0: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("survey_name", "128s"),
("transect_name", "128s"),
("sounder_name", "128s"),
("version", "30s"),
("spare0", "98s"),
("transceiver_count", "l"),
],
1: [("type", "4s"), ("low_date", "L"), ("high_date", "L")],
}
_SimradDatagramParser.__init__(self, "CON", headers)
self._transducer_headers = {
"ER60": self.COMMON_KEYS,
"ES60": self.COMMON_KEYS,
"ES70": self.COMMON_KEYS,
"MBES": [
("channel_id", "128s"),
("beam_type", "l"),
("frequency", "f"),
("reserved1", "f"),
("equivalent_beam_angle", "f"),
("beamwidth_alongship", "f"),
("beamwidth_athwartship", "f"),
("angle_sensitivity_alongship", "f"),
("angle_sensitivity_athwartship", "f"),
("angle_offset_alongship", "f"),
("angle_offset_athwartship", "f"),
("pos_x", "f"),
("pos_y", "f"),
("pos_z", "f"),
("beam_steering_angle_alongship", "f"),
("beam_steering_angle_athwartship", "f"),
("beam_steering_angle_unused", "f"),
("pulse_length", "f"),
("reserved2", "f"),
("spare1", "20s"),
("gain", "f"),
("reserved3", "f"),
("spare2", "20s"),
("sa_correction", "f"),
("reserved4", "f"),
("spare3", "20s"),
("gpt_software_version", "16s"),
("spare4", "28s"),
],
}
def _unpack_contents(self, raw_string, bytes_read, version):
data = {}
round6 = lambda x: round(x, ndigits=6) # noqa
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
# handle Python 3 strings
if (sys.version_info.major > 2) and isinstance(data[field], bytes):
data[field] = data[field].decode("latin_1")
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 0:
data["transceivers"] = {}
for field in ["transect_name", "version", "survey_name", "sounder_name"]:
data[field] = data[field].strip("\x00")
sounder_name = data["sounder_name"]
if sounder_name == "MBES":
_me70_extra_values = struct.unpack("=hLff", data["spare0"][:14])
data["multiplexing"] = _me70_extra_values[0]
data["time_bias"] = _me70_extra_values[1]
data["sound_velocity_avg"] = _me70_extra_values[2]
data["sound_velocity_transducer"] = _me70_extra_values[3]
data["spare0"] = data["spare0"][:14] + data["spare0"][14:].strip("\x00")
else:
data["spare0"] = data["spare0"].strip("\x00")
buf_indx = self.header_size(version)
try:
transducer_header = self._transducer_headers[sounder_name]
_sounder_name_used = sounder_name
except KeyError:
logger.warning(
"Unknown sounder_name: %s, (no one of %s)",
sounder_name,
list(self._transducer_headers.keys()),
)
logger.warning("Will use ER60 transducer config fields as default")
transducer_header = self._transducer_headers["ER60"]
_sounder_name_used = "ER60"
txcvr_header_fields = [x[0] for x in transducer_header]
txcvr_header_fmt = "=" + "".join([x[1] for x in transducer_header])
txcvr_header_size = struct.calcsize(txcvr_header_fmt)
for txcvr_indx in range(1, data["transceiver_count"] + 1):
txcvr_header_values_encoded = struct.unpack(
txcvr_header_fmt,
raw_string[buf_indx : buf_indx + txcvr_header_size], # noqa
)
txcvr_header_values = list(txcvr_header_values_encoded)
for tx_idx, tx_val in enumerate(txcvr_header_values_encoded):
if isinstance(tx_val, bytes):
txcvr_header_values[tx_idx] = tx_val.decode("latin_1")
txcvr = data["transceivers"].setdefault(txcvr_indx, {})
if _sounder_name_used in ["ER60", "ES60", "ES70"]:
for txcvr_field_indx, field in enumerate(txcvr_header_fields[:17]):
txcvr[field] = txcvr_header_values[txcvr_field_indx]
txcvr["pulse_length_table"] = np.fromiter(
list(map(round6, txcvr_header_values[17:22])), "float"
)
txcvr["spare1"] = txcvr_header_values[22]
txcvr["gain_table"] = np.fromiter(
list(map(round6, txcvr_header_values[23:28])), "float"
)
txcvr["spare2"] = txcvr_header_values[28]
txcvr["sa_correction_table"] = np.fromiter(
list(map(round6, txcvr_header_values[29:34])), "float"
)
txcvr["spare3"] = txcvr_header_values[34]
txcvr["gpt_software_version"] = txcvr_header_values[35]
txcvr["spare4"] = txcvr_header_values[36]
elif _sounder_name_used == "MBES":
for txcvr_field_indx, field in enumerate(txcvr_header_fields):
txcvr[field] = txcvr_header_values[txcvr_field_indx]
else:
raise RuntimeError(
"Unknown _sounder_name_used (Should not happen, this is a bug!)"
)
txcvr["channel_id"] = txcvr["channel_id"].strip("\x00")
txcvr["spare1"] = txcvr["spare1"].strip("\x00")
txcvr["spare2"] = txcvr["spare2"].strip("\x00")
txcvr["spare3"] = txcvr["spare3"].strip("\x00")
txcvr["spare4"] = txcvr["spare4"].strip("\x00")
txcvr["gpt_software_version"] = txcvr["gpt_software_version"].strip("\x00")
buf_indx += txcvr_header_size
elif version == 1:
# CON1 only has a single data field: beam_config, holding an xml string
data["beam_config"] = raw_string[self.header_size(version) :].strip("\x00")
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
if data["transceiver_count"] != len(data["transceivers"]):
logger.warning("Mismatch between 'transceiver_count' and actual # of transceivers")
data["transceiver_count"] = len(data["transceivers"])
sounder_name = data["sounder_name"]
if sounder_name == "MBES":
_packed_me70_values = struct.pack(
"=hLff",
data["multiplexing"],
data["time_bias"],
data["sound_velocity_avg"],
data["sound_velocity_transducer"],
)
data["spare0"] = _packed_me70_values + data["spare0"][14:]
for field in self.header_fields(version):
datagram_contents.append(data[field])
try:
transducer_header = self._transducer_headers[sounder_name]
_sounder_name_used = sounder_name
except KeyError:
logger.warning(
"Unknown sounder_name: %s, (no one of %s)",
sounder_name,
list(self._transducer_headers.keys()),
)
logger.warning("Will use ER60 transducer config fields as default")
transducer_header = self._transducer_headers["ER60"]
_sounder_name_used = "ER60"
txcvr_header_fields = [x[0] for x in transducer_header]
txcvr_header_fmt = "=" + "".join([x[1] for x in transducer_header])
txcvr_header_size = struct.calcsize(txcvr_header_fmt) # noqa
for txcvr_indx, txcvr in list(data["transceivers"].items()):
txcvr_contents = []
if _sounder_name_used in ["ER60", "ES60", "ES70"]:
for field in txcvr_header_fields[:17]:
txcvr_contents.append(txcvr[field])
txcvr_contents.extend(txcvr["pulse_length_table"])
txcvr_contents.append(txcvr["spare1"])
txcvr_contents.extend(txcvr["gain_table"])
txcvr_contents.append(txcvr["spare2"])
txcvr_contents.extend(txcvr["sa_correction_table"])
txcvr_contents.append(txcvr["spare3"])
txcvr_contents.extend([txcvr["gpt_software_version"], txcvr["spare4"]])
txcvr_contents_str = struct.pack(txcvr_header_fmt, *txcvr_contents)
elif _sounder_name_used == "MBES":
for field in txcvr_header_fields:
txcvr_contents.append(txcvr[field])
txcvr_contents_str = struct.pack(txcvr_header_fmt, *txcvr_contents)
else:
raise RuntimeError(
"Unknown _sounder_name_used (Should not happen, this is a bug!)"
)
datagram_fmt += "%ds" % (len(txcvr_contents_str))
datagram_contents.append(txcvr_contents_str)
elif version == 1:
for field in self.header_fields(version):
datagram_contents.append(data[field])
datagram_fmt += "%ds" % (len(data["beam_config"]))
datagram_contents.append(data["beam_config"])
return struct.pack(datagram_fmt, *datagram_contents)
class SimradRawParser(_SimradDatagramParser):
"""
Sample Data Datagram parser operates on dictionaries with the following keys:
type: string == 'RAW0'
low_date: long uint representing LSBytes of 64bit NT date
high_date: long uint representing MSBytes of 64bit NT date
timestamp: datetime.datetime object of NT date, assumed to be UTC
channel [short] Channel number
mode [short] 1 = Power only, 2 = Angle only 3 = Power & Angle
transducer_depth [float]
frequency [float]
transmit_power [float]
pulse_length [float]
bandwidth [float]
sample_interval [float]
sound_velocity [float]
absorption_coefficient [float]
heave [float]
roll [float]
pitch [float]
temperature [float]
heading [float]
transmit_mode [short] 0 = Active, 1 = Passive, 2 = Test, -1 = Unknown
spare0 [str]
offset [long]
count [long]
power [numpy array] Unconverted power values (if present)
angle [numpy array] Unconverted angle values (if present)
from_string(str): parse a raw sample datagram
(with leading/trailing datagram size stripped)
to_string(dict): Returns raw string (including leading/trailing size fields)
ready for writing to disk
"""
def __init__(self):
headers = {
0: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("channel", "h"),
("mode", "h"),
("transducer_depth", "f"),
("frequency", "f"),
("transmit_power", "f"),
("pulse_length", "f"),
("bandwidth", "f"),
("sample_interval", "f"),
("sound_velocity", "f"),
("absorption_coefficient", "f"),
("heave", "f"),
("roll", "f"),
("pitch", "f"),
("temperature", "f"),
("heading", "f"),
("transmit_mode", "h"),
("spare0", "6s"),
("offset", "l"),
("count", "l"),
],
3: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("channel_id", "128s"),
("data_type", "h"),
("spare", "2s"),
("offset", "l"),
("count", "l"),
],
4: [
("type", "4s"),
("low_date", "L"),
("high_date", "L"),
("channel_id", "128s"),
("data_type", "h"),
("spare", "2s"),
("offset", "l"),
("count", "l"),
],
}
_SimradDatagramParser.__init__(self, "RAW", headers)
def _unpack_contents(self, raw_string, bytes_read, version):
header_values = struct.unpack(
self.header_fmt(version), raw_string[: self.header_size(version)]
)
data = {}
for indx, field in enumerate(self.header_fields(version)):
data[field] = header_values[indx]
if isinstance(data[field], bytes):
data[field] = data[field].decode()
data["timestamp"] = nt_to_unix((data["low_date"], data["high_date"]))
data["bytes_read"] = bytes_read
if version == 0:
if data["count"] > 0:
block_size = data["count"] * 2
indx = self.header_size(version)
if int(data["mode"]) & 0x1:
data["power"] = np.frombuffer(
raw_string[indx : indx + block_size], dtype="int16" # noqa
)
indx += block_size
else:
data["power"] = None
if int(data["mode"]) & 0x2:
data["angle"] = np.frombuffer(
raw_string[indx : indx + block_size], dtype="int8" # noqa
)
data["angle"] = data["angle"].reshape((-1, 2))
else:
data["angle"] = None
else:
data["power"] = np.empty((0,), dtype="int16")
data["angle"] = np.empty((0, 2), dtype="int8")
# RAW3 and RAW4 have the same format, only Datatype Bit 0-1 not used in RAW4
elif version == 3 or version == 4:
# result = 1j*Data[...,1]; result += Data[...,0]
# clean up the channel ID
data["channel_id"] = data["channel_id"].strip("\x00")
if data["count"] > 0:
# set the initial block size and indx value.
block_size = data["count"] * 2
indx = self.header_size(version)
if data["data_type"] & 0b1:
data["power"] = np.frombuffer(
raw_string[indx : indx + block_size], dtype="int16" # noqa
)
indx += block_size
else:
data["power"] = None
if data["data_type"] & 0b10:
data["angle"] = np.frombuffer(
raw_string[indx : indx + block_size], dtype="int8" # noqa
)
data["angle"] = data["angle"].reshape((-1, 2))
indx += block_size
else:
data["angle"] = None
# determine the complex sample data type - this is contained in bits 2 and 3
# of the datatype <short> value. I'm assuming the types are exclusive...
data["complex_dtype"] = np.float16
type_bytes = 2
if data["data_type"] & 0b1000:
data["complex_dtype"] = np.float32
type_bytes = 8
# determine the number of complex samples
data["n_complex"] = data["data_type"] >> 8
# unpack the complex samples
if data["n_complex"] > 0:
# determine the block size
block_size = data["count"] * data["n_complex"] * type_bytes
data["complex"] = np.frombuffer(
raw_string[indx : indx + block_size], # noqa
dtype=data["complex_dtype"],
)
data["complex"].dtype = np.complex64
else:
data["complex"] = None
else:
data["power"] = np.empty((0,), dtype="int16")
data["angle"] = np.empty((0,), dtype="int8")
data["complex"] = np.empty((0,), dtype="complex64")
data["n_complex"] = 0
return data
def _pack_contents(self, data, version):
datagram_fmt = self.header_fmt(version)
datagram_contents = []
if version == 0:
if data["count"] > 0:
if (int(data["mode"]) & 0x1) and (len(data.get("power", [])) != data["count"]):
logger.warning(
"Data 'count' = %d, but contains %d power samples. Ignoring power."
)
data["mode"] &= ~(1 << 0)
if (int(data["mode"]) & 0x2) and (len(data.get("angle", [])) != data["count"]):
logger.warning(
"Data 'count' = %d, but contains %d angle samples. Ignoring angle."
)
data["mode"] &= ~(1 << 1)
if data["mode"] == 0:
logger.warning(
"Data 'count' = %d, but mode == 0. Setting count to 0",
data["count"],
)
data["count"] = 0
for field in self.header_fields(version):
datagram_contents.append(data[field])
if data["count"] > 0:
if int(data["mode"]) & 0x1:
datagram_fmt += "%dh" % (data["count"])
datagram_contents.extend(data["power"])
if int(data["mode"]) & 0x2:
datagram_fmt += "%dH" % (data["count"])
datagram_contents.extend(data["angle"])
return struct.pack(datagram_fmt, *datagram_contents)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,814 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/convert/test_convert_azfp.py | """test_convert_azfp.py
This module contains tests that:
- verify echopype converted files against those from AZFP Matlab scripts and EchoView
- convert AZFP file with different range settings across frequency
"""
import numpy as np
import pandas as pd
from scipy.io import loadmat
from echopype import open_raw
import pytest
@pytest.fixture
def azfp_path(test_path):
return test_path["AZFP"]
def check_platform_required_scalar_vars(echodata):
# check convention-required variables in the Platform group
for var in [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z",
]:
assert var in echodata["Platform"]
assert np.isnan(echodata["Platform"][var])
def test_convert_azfp_01a_matlab_raw(azfp_path):
"""Compare parsed raw data with Matlab outputs."""
azfp_01a_path = azfp_path / '17082117.01A'
azfp_xml_path = azfp_path / '17041823.XML'
azfp_matlab_data_path = azfp_path / 'from_matlab/17082117_matlab_Data.mat'
azfp_matlab_output_path = azfp_path / 'from_matlab/17082117_matlab_Output_Sv.mat'
# Convert file
echodata = open_raw(
raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path
)
# Read in the dataset that will be used to confirm working conversions. (Generated by Matlab)
ds_matlab = loadmat(azfp_matlab_data_path)
ds_matlab_output = loadmat(azfp_matlab_output_path)
# Test beam group
# frequency
assert np.array_equal(
ds_matlab['Data']['Freq'][0][0].squeeze(),
echodata["Sonar/Beam_group1"].frequency_nominal / 1000,
) # matlab file in kHz
# backscatter count
assert np.array_equal(
np.array(
[ds_matlab_output['Output'][0]['N'][fidx] for fidx in range(4)]
),
echodata["Sonar/Beam_group1"].backscatter_r.values,
)
# Test vendor group
# Test temperature
assert np.array_equal(
np.array([d[4] for d in ds_matlab['Data']['Ancillary'][0]]).squeeze(),
echodata["Vendor_specific"].ancillary.isel(ancillary_len=4).values,
)
assert np.array_equal(
np.array([d[0] for d in ds_matlab['Data']['BatteryTx'][0]]).squeeze(),
echodata["Vendor_specific"].battery_tx,
)
assert np.array_equal(
np.array(
[d[0] for d in ds_matlab['Data']['BatteryMain'][0]]
).squeeze(),
echodata["Vendor_specific"].battery_main,
)
# tilt x-y
assert np.array_equal(
np.array([d[0] for d in ds_matlab['Data']['Ancillary'][0]]).squeeze(),
echodata["Vendor_specific"].tilt_x_count,
)
assert np.array_equal(
np.array([d[1] for d in ds_matlab['Data']['Ancillary'][0]]).squeeze(),
echodata["Vendor_specific"].tilt_y_count,
)
# check convention-required variables in the Platform group
check_platform_required_scalar_vars(echodata)
def test_convert_azfp_01a_matlab_derived():
"""Compare variables derived from raw parsed data with Matlab outputs."""
# TODO: test derived data
# - ds_beam.ping_time from 01A raw data records
# - investigate why ds_beam.tilt_x/y are different from ds_matlab['Data']['Tx']/['Ty']
# - derived temperature
# # check convention-required variables in the Platform group
# check_platform_required_scalar_vars(echodata)
pytest.xfail("Tests for converting AZFP and comparing it"
+ " against Matlab derived data have not been implemented yet.")
def test_convert_azfp_01a_raw_echoview(azfp_path):
"""Compare parsed power data (count) with csv exported by EchoView."""
azfp_01a_path = azfp_path / '17082117.01A'
azfp_xml_path = azfp_path / '17041823.XML'
# Read csv files exported by EchoView
azfp_csv_path = [
azfp_path / f"from_echoview/17082117-raw{freq}.csv"
for freq in [38, 125, 200, 455]
]
channels = []
for file in azfp_csv_path:
channels.append(
pd.read_csv(file, header=None, skiprows=[0]).iloc[:, 6:]
)
test_power = np.stack(channels)
# Convert to netCDF and check
echodata = open_raw(
raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path
)
assert np.array_equal(test_power, echodata["Sonar/Beam_group1"].backscatter_r)
# check convention-required variables in the Platform group
check_platform_required_scalar_vars(echodata)
def test_convert_azfp_01a_different_ranges(azfp_path):
"""Test converting files with different range settings across frequency."""
azfp_01a_path = azfp_path / '17031001.01A'
azfp_xml_path = azfp_path / '17030815.XML'
# Convert file
echodata = open_raw(
raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path
)
assert echodata["Sonar/Beam_group1"].backscatter_r.sel(channel='55030-125-1').dropna(
'range_sample'
).shape == (360, 438)
assert echodata["Sonar/Beam_group1"].backscatter_r.sel(channel='55030-769-4').dropna(
'range_sample'
).shape == (360, 135)
# check convention-required variables in the Platform group
check_platform_required_scalar_vars(echodata)
def test_convert_azfp_01a_notemperature_notilt(azfp_path):
"""Test converting file with no valid temperature or tilt data."""
azfp_01a_path = azfp_path / 'rutgers_glider_notemperature/22052500.01A'
azfp_xml_path = azfp_path / 'rutgers_glider_notemperature/22052501.XML'
echodata = open_raw(
raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path
)
# Temperature variable is present in the Environment group and its values are all nan
assert "temperature" in echodata["Environment"]
assert echodata["Environment"]["temperature"].isnull().all()
# Tilt variables are present in the Platform group and their values are all nan
assert "tilt_x" in echodata["Platform"]
assert "tilt_y" in echodata["Platform"]
assert echodata["Platform"]["tilt_x"].isnull().all()
assert echodata["Platform"]["tilt_y"].isnull().all()
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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", 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"/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,815 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_calibrate.py | import numpy as np
import pandas as pd
import pytest
from scipy.io import loadmat
import echopype as ep
from echopype.calibrate.env_params_old import EnvParams
import xarray as xr
@pytest.fixture
def azfp_path(test_path):
return test_path['AZFP']
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
@pytest.fixture
def ek80_path(test_path):
return test_path['EK80']
@pytest.fixture
def ek80_cal_path(test_path):
return test_path['EK80_CAL']
@pytest.fixture
def ek80_ext_path(test_path):
return test_path['EK80_EXT']
def test_compute_Sv_returns_water_level(ek60_path):
# get EchoData object that has the water_level variable under platform and compute Sv of it
ed = ep.open_raw(ek60_path / "ncei-wcsd" / "Summer2017-D20170620-T011027.raw", "EK60")
ds_Sv = ep.calibrate.compute_Sv(ed)
# make sure the returned Dataset has water_level and throw an assertion error if the
# EchoData object does not have water_level (just in case we remove it from the file
# used in the future)
assert 'water_level' in ed["Platform"].data_vars.keys()
assert 'water_level' in ds_Sv.data_vars
def test_compute_Sv_ek60_echoview(ek60_path):
# constant range_sample
ek60_raw_path = str(
ek60_path.joinpath('DY1801_EK60-D20180211-T164025.raw')
)
ek60_echoview_path = ek60_path.joinpath('from_echoview')
# Convert file
echodata = ep.open_raw(ek60_raw_path, sonar_model='EK60')
# Calibrate to get Sv
ds_Sv = ep.calibrate.compute_Sv(echodata)
# Compare with EchoView outputs
channels = []
for freq in [18, 38, 70, 120, 200]:
fname = str(
ek60_echoview_path.joinpath(
'DY1801_EK60-D20180211-T164025-Sv%d.csv' % freq
)
)
channels.append(
pd.read_csv(fname, header=None, skiprows=[0]).iloc[:, 13:]
)
test_Sv = np.stack(channels)
# Echoview data is shifted by 1 sample along range (missing the first sample)
# TODO: resolve: pydevd warning: Computing repr of channels (list) was slow (took 0.29s)
assert np.allclose(
test_Sv[:, :, 7:],
ds_Sv.Sv.isel(ping_time=slice(None, 10), range_sample=slice(8, None)),
atol=1e-8
)
def test_compute_Sv_ek60_matlab(ek60_path):
ek60_raw_path = str(
ek60_path.joinpath('DY1801_EK60-D20180211-T164025.raw')
)
ek60_matlab_path = str(
ek60_path.joinpath('from_matlab', 'DY1801_EK60-D20180211-T164025.mat')
)
# Convert file
echodata = ep.open_raw(ek60_raw_path, sonar_model='EK60')
# Calibrate to get Sv
ds_Sv = ep.calibrate.compute_Sv(echodata)
ds_TS = ep.calibrate.compute_TS(echodata)
# Load matlab outputs and test
# matlab outputs were saved using
# save('from_matlab/DY1801_EK60-D20180211-T164025.mat', 'data')
ds_base = loadmat(ek60_matlab_path)
def check_output(da_cmp, cal_type):
# ds_base["data"]["pings"][0][0]["Sv"].shape = (1, 5) [5 channels]
for seq, ch in enumerate(ds_base["data"]["config"][0][0]["channelid"][0]):
ep_vals = da_cmp.sel(channel=ch).squeeze().data[:, 8:] # ignore the first 8 samples
pyel_vals = ds_base['data']['pings'][0][0][cal_type][0, seq].T[:, 8:]
assert np.allclose(pyel_vals, ep_vals)
# Check Sv
check_output(ds_Sv['Sv'], 'Sv')
# Check TS
check_output(ds_TS['TS'], 'Sp')
def test_compute_Sv_ek60_duplicated_freq(ek60_path):
# TODO: add comparison of actual values in this test
ek60_raw_path = str(
ek60_path.joinpath('DY1002_EK60-D20100318-T023008_rep_freq.raw')
)
# Convert file
echodata = ep.open_raw(ek60_raw_path, sonar_model='EK60')
# Calibrate to get Sv
ds_Sv = ep.calibrate.compute_Sv(echodata)
ds_TS = ep.calibrate.compute_TS(echodata)
assert isinstance(ds_Sv, xr.Dataset)
assert isinstance(ds_TS, xr.Dataset)
def test_compute_Sv_azfp(azfp_path):
azfp_01a_path = str(azfp_path.joinpath('17082117.01A'))
azfp_xml_path = str(azfp_path.joinpath('17041823.XML'))
azfp_matlab_Sv_path = str(
azfp_path.joinpath('from_matlab', '17082117_matlab_Output_Sv.mat')
)
azfp_matlab_TS_path = str(
azfp_path.joinpath('from_matlab', '17082117_matlab_Output_TS.mat')
)
# Convert to .nc file
echodata = ep.open_raw(
raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path
)
# Calibrate using identical env params as in Matlab ParametersAZFP.m
# AZFP Matlab code uses average temperature
avg_temperature = echodata["Environment"]['temperature'].values.mean()
env_params = {
'temperature': avg_temperature,
'salinity': 27.9,
'pressure': 59,
}
ds_Sv = ep.calibrate.compute_Sv(echodata=echodata, env_params=env_params)
ds_TS = ep.calibrate.compute_TS(echodata=echodata, env_params=env_params)
# Load matlab outputs and test
# matlab outputs were saved using
# save('from_matlab/17082117_matlab_Output.mat', 'Output') # data variables
# save('from_matlab/17082117_matlab_Par.mat', 'Par') # parameters
def check_output(base_path, ds_cmp, cal_type):
ds_base = loadmat(base_path)
# print(f"ds_base = {ds_base}")
cal_type_in_ds_cmp = {
'Sv': 'Sv',
'TS': 'TS', # TS here is TS in matlab outputs
}
for fidx in range(4): # loop through all freq
assert np.alltrue(
ds_cmp.echo_range.isel(channel=fidx, ping_time=0).values[None, :]
== ds_base['Output'][0]['Range'][fidx]
)
assert np.allclose(
ds_cmp[cal_type_in_ds_cmp[cal_type]].isel(channel=fidx).values,
ds_base['Output'][0][cal_type][fidx],
atol=1e-13,
rtol=0,
)
# Check Sv
check_output(base_path=azfp_matlab_Sv_path, ds_cmp=ds_Sv, cal_type='Sv')
# Check TS
check_output(base_path=azfp_matlab_TS_path, ds_cmp=ds_TS, cal_type='TS')
def test_compute_Sv_ek80_CW_complex(ek80_path):
"""Test calibrate CW mode data encoded as complex samples."""
ek80_raw_path = str(
ek80_path.joinpath('ar2.0-D20201210-T000409.raw')
) # CW complex
echodata = ep.open_raw(ek80_raw_path, sonar_model='EK80')
ds_Sv = ep.calibrate.compute_Sv(
echodata, waveform_mode='CW', encode_mode='complex'
)
assert isinstance(ds_Sv, xr.Dataset) is True
ds_TS = ep.calibrate.compute_TS(
echodata, waveform_mode='CW', encode_mode='complex'
)
assert isinstance(ds_TS, xr.Dataset) is True
def test_compute_Sv_ek80_BB_complex(ek80_path):
"""Test calibrate BB mode data encoded as complex samples."""
ek80_raw_path = str(
ek80_path.joinpath('ar2.0-D20201209-T235955.raw')
) # CW complex
echodata = ep.open_raw(ek80_raw_path, sonar_model='EK80')
ds_Sv = ep.calibrate.compute_Sv(
echodata, waveform_mode='BB', encode_mode='complex'
)
assert isinstance(ds_Sv, xr.Dataset) is True
ds_TS = ep.calibrate.compute_TS(
echodata, waveform_mode='BB', encode_mode='complex'
)
assert isinstance(ds_TS, xr.Dataset) is True
def test_compute_Sv_ek80_CW_power_BB_complex(ek80_path):
"""
Tests calibration in CW mode data encoded as power samples
and calibration in BB mode data encoded as complex samples,
while the file contains both CW power and BB complex samples.
"""
ek80_raw_path = ek80_path / "Summer2018--D20180905-T033113.raw"
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="CW", encode_mode="power"
)
assert isinstance(ds_Sv, xr.Dataset)
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="BB", encode_mode="complex"
)
assert isinstance(ds_Sv, xr.Dataset)
def test_compute_Sv_ek80_CW_complex_BB_complex(ek80_cal_path, ek80_path):
"""
Tests calibration for file containing both BB and CW mode data
with both encoded as complex samples.
"""
ek80_raw_path = ek80_cal_path / "2018115-D20181213-T094600.raw" # rx impedance / rx fs / tcvr type
# ek80_raw_path = ek80_path / "D20170912-T234910.raw" # rx impedance / rx fs / tcvr type
# ek80_raw_path = ek80_path / "Summer2018--D20180905-T033113.raw" # BB only, rx impedance / rx fs / tcvr type
# ek80_raw_path = ek80_path / "ar2.0-D20201210-T000409.raw" # CW only, rx impedance / rx fs / tcvr type
# ek80_raw_path = ek80_path / "saildrone/SD2019_WCS_v05-Phase0-D20190617-T125959-0.raw" # rx impedance / tcvr type
# ek80_raw_path = ek80_path / "D20200528-T125932.raw" # CW only, WBT MINI, rx impedance / rx fs / tcvr type
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
# ds_Sv = ep.calibrate.compute_Sv(
# ed, waveform_mode="CW", encode_mode="complex"
# )
# assert isinstance(ds_Sv, xr.Dataset)
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="BB", encode_mode="complex"
)
assert isinstance(ds_Sv, xr.Dataset)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,816 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/range.py | from typing import Dict
import xarray as xr
from ..echodata import EchoData
from ..echodata.simrad import retrieve_correct_beam_group
from .env_params import harmonize_env_param_time
DIMENSION_ORDER = ["channel", "ping_time", "range_sample"]
def compute_range_AZFP(echodata: EchoData, env_params: Dict, cal_type: str) -> xr.DataArray:
"""
Computes the range (``echo_range``) of AZFP backscatter data in meters.
Parameters
----------
echodata : EchoData
An EchoData object holding data from an AZFP echosounder
env_params : dict
A dictionary holding environmental parameters needed for computing range
See echopype.calibrate.env_params.get_env_params_AZFP()
cal_type : {"Sv", "TS"}
- `"Sv"` for calculating volume backscattering strength
- `"TS"` for calculating target strength.
This parameter needs to be specified for data from the AZFP echosounder
due to a difference in the range computation given by the manufacturer
Returns
-------
xr.DataArray
The range (``echo_range``) of the data in meters.
Notes
-----
For AZFP echosounder, the returned ``echo_range`` is duplicated along ``ping_time``
to conform with outputs from other echosounders, even though within each data
file the range is held constant.
"""
# sound_speed should exist already
if "sound_speed" not in env_params:
raise RuntimeError(
"sounds_speed not included in env_params, "
"use echopype.calibrate.env_params.get_env_params_AZFP() to compute env_params "
"by supplying temperature, salinity, and pressure."
)
else:
sound_speed = env_params["sound_speed"]
# Check cal_type
if cal_type is None:
raise ValueError('cal_type must be "Sv" or "TS"')
# Groups to use
vend = echodata["Vendor_specific"]
beam = echodata["Sonar/Beam_group1"]
# Notation below follows p.86 of user manual
N = vend["number_of_samples_per_average_bin"] # samples per bin
f = vend["digitization_rate"] # digitization rate
L = vend["lockout_index"] # number of lockout samples
# keep this in ref of AZFP matlab code,
# set to 1 since we want to calculate from raw data
bins_to_avg = 1
# Harmonize sound_speed time1 and Beam_group1 ping_time
sound_speed = harmonize_env_param_time(
p=sound_speed,
ping_time=beam.ping_time,
)
# Calculate range using parameters for each freq
# This is "the range to the centre of the sampling volume
# for bin m" from p.86 of user manual
if cal_type == "Sv":
range_offset = 0
else:
range_offset = sound_speed * beam["transmit_duration_nominal"] / 4 # from matlab code
range_meter = (
sound_speed * L / (2 * f)
+ (sound_speed / 4)
* (
((2 * (beam["range_sample"] + 1) - 1) * N * bins_to_avg - 1) / f
+ beam["transmit_duration_nominal"]
)
- range_offset
)
# add name to facilitate xr.merge
range_meter.name = "echo_range"
# make order of dims conform with the order of backscatter data
return range_meter.transpose(*DIMENSION_ORDER)
def compute_range_EK(
echodata: EchoData,
env_params: Dict,
waveform_mode: str = "CW",
encode_mode: str = "power",
chan_sel=None,
):
"""
Computes the range (``echo_range``) of EK backscatter data in meters.
Parameters
----------
echodata : EchoData
An EchoData object holding data from an AZFP echosounder
env_params : dict
A dictionary holding environmental parameters needed for computing range
See echopype.calibrate.env_params.get_env_params_EK
waveform_mode : {"CW", "BB"}
Type of transmit waveform.
Required only for data from the EK80 echosounder.
- `"CW"` for narrowband transmission,
returned echoes recorded either as complex or power/angle samples
- `"BB"` for broadband transmission,
returned echoes recorded as complex samples
encode_mode : {"complex", "power"}
Type of encoded data format.
Required only for data from the EK80 echosounder.
- `"complex"` for complex samples
- `"power"` for power/angle samples, only allowed when
the echosounder is configured for narrowband transmission
Returns
-------
xr.DataArray
The range (``echo_range``) of the data in meters.
Notes
-----
The EK80 echosounder can be configured to transmit
either broadband (``waveform_mode="BB"``) or narrowband (``waveform_mode="CW"``) signals.
When transmitting in broadband mode, the returned echoes must be
encoded as complex samples (``encode_mode="complex"``).
When transmitting in narrowband mode, the returned echoes can be encoded
either as complex samples (``encode_mode="complex"``)
or as power/angle combinations (``encode_mode="power"``) in a format
similar to those recorded by EK60 echosounders (the "power/angle" format).
"""
# sound_speed should exist already
if echodata.sonar_model in ("EK60", "ES70"):
ek_str = "EK60"
elif echodata.sonar_model in ("EK80", "ES80", "EA640"):
ek_str = "EK80"
else:
raise ValueError("The specified sonar_model is not supported!")
if "sound_speed" not in env_params:
raise RuntimeError(
"sounds_speed not included in env_params, "
f"use echopype.calibrate.env_params.get_env_params_{ek_str}() to compute env_params "
)
else:
sound_speed = env_params["sound_speed"]
# Get the right Sonar/Beam_groupX group according to encode_mode
ed_beam_group = retrieve_correct_beam_group(echodata, waveform_mode, encode_mode)
beam = (
echodata[ed_beam_group]
if chan_sel is None
else echodata[ed_beam_group].sel(channel=chan_sel)
)
# Harmonize sound_speed time1 and Beam_groupX ping_time
sound_speed = harmonize_env_param_time(
p=sound_speed,
ping_time=beam.ping_time,
)
# Range in meters, not modified for TVG compensation
range_meter = beam["range_sample"] * beam["sample_interval"] * sound_speed / 2
# make order of dims conform with the order of backscatter data
range_meter = range_meter.transpose(*DIMENSION_ORDER)
# set entries with NaN backscatter data to NaN
if "beam" in beam["backscatter_r"].dims:
# Drop beam because echo_range should not have a beam dimension
valid_idx = ~beam["backscatter_r"].isel(beam=0).drop("beam").isnull()
else:
valid_idx = ~beam["backscatter_r"].isnull()
range_meter = range_meter.where(valid_idx)
# remove time1 if exists as a coordinate
if "time1" in range_meter.coords:
range_meter = range_meter.drop("time1")
# add name to facilitate xr.merge
range_meter.name = "echo_range"
return range_meter
def range_mod_TVG_EK(
echodata: EchoData, ed_beam_group: str, range_meter: xr.DataArray, sound_speed: xr.DataArray
) -> xr.DataArray:
"""
Modify range for TVG calculation.
TVG correction factor changes depending when the echo recording starts
wrt when the transmit signal is sent out.
This depends on whether it is Ex60 or Ex80 style hardware
ref: https://github.com/CI-CMG/pyEcholab/blob/RHT-EK80-Svf/echolab2/instruments/EK80.py#L4297-L4308 # noqa
"""
def mod_Ex60():
# 2-sample shift in the beginning
return 2 * beam["sample_interval"] * sound_speed / 2 # [frequency x range_sample]
def mod_Ex80():
mod = sound_speed * beam["transmit_duration_nominal"] / 4
if isinstance(mod, xr.DataArray) and "time1" in mod.coords:
mod = mod.squeeze().drop("time1")
return mod
beam = echodata[ed_beam_group]
vend = echodata["Vendor_specific"]
# If EK60
if echodata.sonar_model in ["EK60", "ES70"]:
range_meter = range_meter - mod_Ex60()
# If EK80:
# - compute range first assuming all channels have Ex80 style hardware
# - change range for channels with Ex60 style hardware (GPT)
elif echodata.sonar_model in ["EK80", "ES80", "EA640"]:
range_meter = range_meter - mod_Ex80()
# Change range for all channels with GPT
if "GPT" in vend["transceiver_type"]:
ch_GPT = vend["transceiver_type"] == "GPT"
range_meter.loc[dict(channel=ch_GPT)] = range_meter.sel(channel=ch_GPT) - mod_Ex60()
return range_meter
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,817 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_env_params.py | import pytest
import numpy as np
import xarray as xr
import echopype as ep
from echopype.calibrate.env_params import (
harmonize_env_param_time,
sanitize_user_env_dict,
ENV_PARAMS,
get_env_params_AZFP,
get_env_params_EK,
)
@pytest.fixture
def azfp_path(test_path):
return test_path['AZFP']
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
@pytest.fixture
def ek80_cal_path(test_path):
return test_path['EK80_CAL']
def test_harmonize_env_param_time():
# Scalar
p = 10.05
assert harmonize_env_param_time(p=p) == 10.05
# time1 length=1, should return length=1 numpy array
p = xr.DataArray(
data=[1],
coords={
"time1": np.array(["2017-06-20T01:00:00"], dtype="datetime64[ns]")
},
dims=["time1"]
)
assert harmonize_env_param_time(p=p) == 1
# time1 length>1, interpolate to tareget ping_time
p = xr.DataArray(
data=np.array([0, 1]),
coords={
"time1": np.arange("2017-06-20T01:00:00", "2017-06-20T01:00:31", np.timedelta64(30, "s"), dtype="datetime64[ns]")
},
dims=["time1"]
)
# ping_time target is identical to time1
ping_time_target = p["time1"].rename({"time1": "ping_time"})
p_new = harmonize_env_param_time(p=p, ping_time=ping_time_target)
assert (p_new["ping_time"] == ping_time_target).all()
assert (p_new.data == p.data).all()
# ping_time target requires actual interpolation
ping_time_target = xr.DataArray(
data=[1],
coords={
"ping_time": np.array(["2017-06-20T01:00:15"], dtype="datetime64[ns]")
},
dims=["ping_time"]
)
p_new = harmonize_env_param_time(p=p, ping_time=ping_time_target["ping_time"])
assert p_new["ping_time"] == ping_time_target["ping_time"]
assert p_new.data == 0.5
@pytest.mark.parametrize(
("user_dict", "channel", "out_dict"),
[
# dict all scalars, channel a list, output should be all scalars
# - this behavior departs from sanitize_user_cal_dict, which will make scalars into xr.DataArray
(
{"temperature": 10, "salinity": 20},
["chA", "chB"],
dict(
dict.fromkeys(ENV_PARAMS), **{"temperature": 10, "salinity": 20}
)
),
# dict has xr.DataArray, channel a list with matching values with those in dict
(
{"temperature": 10, "sound_absorption": xr.DataArray([10, 20], coords={"channel": ["chA", "chB"]})},
["chA", "chB"],
dict(
dict.fromkeys(ENV_PARAMS),
**{"temperature": 10, "sound_absorption": xr.DataArray([10, 20], coords={"channel": ["chA", "chB"]})}
)
),
# dict has xr.DataArray, channel a list with non-matching values with those in dict: XFAIL
pytest.param(
{"temperature": 10, "sound_absorption": xr.DataArray([10, 20], coords={"channel": ["chA", "chB"]})},
["chA", "chC"], None,
marks=pytest.mark.xfail(strict=True, reason="channel coordinate in param xr.DataArray mismatches that in the channel list"),
),
# dict has xr.DataArray, channel a xr.DataArray
(
{"temperature": 10, "sound_absorption": xr.DataArray([10, 20], coords={"channel": ["chA", "chB"]})},
xr.DataArray(["chA", "chB"], coords={"channel": ["chA", "chB"]}),
dict(
dict.fromkeys(ENV_PARAMS),
**{"temperature": 10, "sound_absorption": xr.DataArray([10, 20], coords={"channel": ["chA", "chB"]})}
)
),
# dict has sound_absorption as a scalar: XFAIL
pytest.param(
{"temperature": 10, "sound_absorption": 0.02},
["chA", "chB"], None,
marks=pytest.mark.xfail(strict=True, reason="sound_absorption should be a list or an xr.DataArray"),
),
],
ids=[
"in_scalar_channel_list_out_scalar",
"in_da_channel_list_out_da",
"in_da_channel_list_mismatch",
"in_da_channel_da",
"in_absorption_scalae"
]
)
def test_sanitize_user_env_dict(user_dict, channel, out_dict):
"""
Only test the case where the input sound_absorption is not an xr.DataArray nor a list,
since other cases are tested under test_cal_params::test_sanitize_user_cal_dict
"""
env_dict = sanitize_user_env_dict(user_dict, channel)
for p, v in env_dict.items():
if isinstance(v, xr.DataArray):
assert v.identical(out_dict[p])
else:
assert v == out_dict[p]
@pytest.mark.parametrize(
("env_ext", "out_dict"),
[
# pH should not exist in the output Sv dataset, formula sources should both be AZFP
(
{"temperature": 10, "salinity": 20, "pressure": 100, "pH": 8.1},
dict(
dict.fromkeys(ENV_PARAMS), **{"temperature": 10, "salinity": 20, "pressure": 100}
)
),
# not including salinity or pressure: XFAIL
pytest.param(
{"temperature": 10, "pressure": 100, "pH": 8.1}, None,
marks=pytest.mark.xfail(strict=True, reason="Fail since cal_channel_id in input param does not match channel of data"),
),
],
ids=[
"default",
"no_salinity",
]
)
def test_get_env_params_AZFP(azfp_path, env_ext, out_dict):
azfp_01a_path = str(azfp_path.joinpath('17082117.01A'))
azfp_xml_path = str(azfp_path.joinpath('17041823.XML'))
ed = ep.open_raw(azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path)
env_dict = get_env_params_AZFP(echodata=ed, user_dict=env_ext)
out_dict = dict(
out_dict,
**{
"sound_speed": ep.utils.uwa.calc_sound_speed(
temperature=env_dict["temperature"],
salinity=env_dict["salinity"],
pressure=env_dict["pressure"],
formula_source="AZFP"
),
"sound_absorption": ep.utils.uwa.calc_absorption(
frequency=ed["Sonar/Beam_group1"]["frequency_nominal"],
temperature=env_dict["temperature"],
salinity=env_dict["salinity"],
pressure=env_dict["pressure"],
formula_source="AZFP",
),
"formula_sound_speed": "AZFP",
"formula_absorption": "AZFP",
}
)
assert "pH" not in env_dict
assert env_dict["formula_absorption"] == "AZFP"
assert env_dict["formula_sound_speed"] == "AZFP"
for p, v in env_dict.items():
if isinstance(v, xr.DataArray):
assert v.identical(out_dict[p])
else:
assert v == out_dict[p]
@pytest.mark.parametrize(
("env_ext", "ref_formula_sound_speed", "ref_formula_absorption"),
[
# T, S, P, pH all exist so will trigger calculation, check default formula sources
(
{"temperature": 10, "salinity": 30, "pressure": 100, "pH": 8.1},
"Mackenzie", "FG",
),
# T, S, P, pH all exist, will calculate; has absorption formula passed in, check using the correct formula
(
{"temperature": 10, "salinity": 30, "pressure": 100, "pH": 8.1, "formula_absorption": "AM"},
"Mackenzie", "AM",
),
],
ids=[
"calc_no_formula",
"calc_with_formula",
]
)
def test_get_env_params_EK60_calculate(ek60_path, env_ext, ref_formula_sound_speed, ref_formula_absorption):
ed = ep.open_raw(ek60_path / "ncei-wcsd" / "Summer2017-D20170620-T011027.raw", sonar_model="EK60")
env_dict = get_env_params_EK(
sonar_type="EK60",
beam=ed["Sonar/Beam_group1"],
env=ed["Environment"],
user_dict=env_ext,
)
# Check formula sources
assert env_dict["formula_sound_speed"] == ref_formula_sound_speed
assert env_dict["formula_absorption"] == ref_formula_absorption
# Check computation results
sound_speed_ref = ep.utils.uwa.calc_sound_speed(
temperature=env_ext["temperature"],
salinity=env_ext["salinity"],
pressure=env_ext["pressure"],
formula_source=ref_formula_sound_speed,
)
sound_speed_ref = ep.calibrate.env_params.harmonize_env_param_time(
sound_speed_ref, ping_time=ed["Sonar/Beam_group1"]["ping_time"]
)
absorption_ref = ep.utils.uwa.calc_absorption(
frequency=ed["Sonar/Beam_group1"]["frequency_nominal"],
temperature=env_ext["temperature"],
salinity=env_ext["salinity"],
pressure=env_ext["pressure"],
pH=env_ext["pH"],
sound_speed=sound_speed_ref,
formula_source=ref_formula_absorption,
)
absorption_ref = ep.calibrate.env_params.harmonize_env_param_time(
absorption_ref, ping_time=ed["Sonar/Beam_group1"]["ping_time"]
)
assert env_dict["sound_speed"] == sound_speed_ref
assert env_dict["sound_absorption"].identical(absorption_ref)
def test_get_env_params_EK60_from_data(ek60_path):
"""
If one of T, S, P, pH does not exist, use values from data file
"""
ed = ep.open_raw(ek60_path / "ncei-wcsd" / "Summer2017-D20170620-T011027.raw", sonar_model="EK60")
env_dict = get_env_params_EK(
sonar_type="EK60",
beam=ed["Sonar/Beam_group1"],
env=ed["Environment"],
user_dict={"temperature": 10},
)
# Check default formula sources
assert "formula_sound_speed" not in env_dict
assert "formula_absorption" not in env_dict
# Check params from data file: need to make time1 --> ping_time
ref_sound_speed = ed["Environment"]["sound_speed_indicative"].copy()
ref_sound_speed.coords["ping_time"] = ref_sound_speed["time1"]
ref_sound_speed = ref_sound_speed.swap_dims({"time1": "ping_time"}).drop_vars("time1")
assert env_dict["sound_speed"].identical(ref_sound_speed)
ref_absorption = ed["Environment"]["absorption_indicative"].copy()
ref_absorption.coords["ping_time"] = ref_absorption["time1"]
ref_absorption = ref_absorption.swap_dims({"time1": "ping_time"}).drop_vars("time1")
assert env_dict["sound_absorption"].identical(ref_absorption)
@pytest.mark.parametrize(
("env_ext", "ref_formula_sound_speed", "ref_formula_absorption"),
[
# T, S, P, pH all exist, check default formula sources
(
{"temperature": 10, "salinity": 30, "pressure": 100, "pH": 8.1},
"Mackenzie", "FG",
),
# T, S, P, pH all exist; has absorption formula passed in, check using the correct formula
(
{"temperature": 10, "salinity": 30, "pressure": 100, "pH": 8.1, "formula_absorption": "AM"},
"Mackenzie", "AM",
),
],
ids=[
"calc_no_formula",
"calc_with_formula",
]
)
def test_get_env_params_EK80_calculate(ek80_cal_path, env_ext, ref_formula_sound_speed, ref_formula_absorption):
ed = ep.open_raw(ek80_cal_path / "2018115-D20181213-T094600.raw", sonar_model="EK80")
env_dict = get_env_params_EK(
sonar_type="EK60",
beam=ed["Sonar/Beam_group1"],
env=ed["Environment"],
user_dict=env_ext,
)
# Check formula sources
assert env_dict["formula_sound_speed"] == ref_formula_sound_speed
assert env_dict["formula_absorption"] == ref_formula_absorption
# Check computation results
sound_speed_ref = ep.utils.uwa.calc_sound_speed(
temperature=env_ext["temperature"],
salinity=env_ext["salinity"],
pressure=env_ext["pressure"],
formula_source=ref_formula_sound_speed,
)
sound_speed_ref = ep.calibrate.env_params.harmonize_env_param_time(
sound_speed_ref, ping_time=ed["Sonar/Beam_group1"]["ping_time"]
)
absorption_ref = ep.utils.uwa.calc_absorption(
frequency=ed["Sonar/Beam_group1"]["frequency_nominal"],
temperature=env_ext["temperature"],
salinity=env_ext["salinity"],
pressure=env_ext["pressure"],
pH=env_ext["pH"],
sound_speed=sound_speed_ref,
formula_source=ref_formula_absorption,
)
absorption_ref = ep.calibrate.env_params.harmonize_env_param_time(
absorption_ref, ping_time=ed["Sonar/Beam_group1"]["ping_time"]
)
assert env_dict["sound_speed"] == sound_speed_ref
assert env_dict["sound_absorption"].identical(absorption_ref)
@pytest.mark.parametrize(
("env_ext", "ref_formula_sound_speed", "ref_formula_absorption"),
[
# Only T exists, so use S, P, pH from data;
# check default formula sources
(
{"temperature": 10},
"Mackenzie", "FG",
),
# Only T exists, so use S, P, pH from data;
# has absorption formula passed in, check using the correct formula
(
{"temperature": 10, "formula_absorption": "AM"},
"Mackenzie", "AM",
),
],
ids=[
"calc_no_formula",
"calc_with_formula",
]
)
def test_get_env_params_EK80_from_data(ek80_cal_path, env_ext, ref_formula_sound_speed, ref_formula_absorption):
ed = ep.open_raw(ek80_cal_path / "2018115-D20181213-T094600.raw", sonar_model="EK80")
env_dict = get_env_params_EK(
sonar_type="EK80",
beam=ed["Sonar/Beam_group1"],
env=ed["Environment"],
user_dict=env_ext,
# technically should use center freq, use frequency nominal here for convenience
freq=ed["Sonar/Beam_group1"]["frequency_nominal"],
)
# Check formula sources
assert "formula_sound_speed" not in env_dict
assert env_dict["formula_absorption"] == ref_formula_absorption
# Check computation results
# Use sound speed from data when T, S, P, pH are not all provided
sound_speed_ref = ed["Environment"]["sound_speed_indicative"]
# Always compute absorption for EK80
absorption_ref = ep.utils.uwa.calc_absorption(
frequency=ed["Sonar/Beam_group1"]["frequency_nominal"],
temperature=env_ext["temperature"], # use user-provided value if exists
salinity=ed["Environment"]["salinity"],
pressure=ed["Environment"]["depth"],
pH=ed["Environment"]["acidity"],
sound_speed=sound_speed_ref,
formula_source=ref_formula_absorption,
)
absorption_ref = ep.calibrate.env_params.harmonize_env_param_time(
absorption_ref, ping_time=ed["Sonar/Beam_group1"]["ping_time"]
)
assert env_dict["sound_speed"] == sound_speed_ref
assert env_dict["sound_absorption"].identical(absorption_ref)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,818 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/set_groups_base.py | import abc
import warnings
from typing import List, Set, Tuple
import dask.array
import numpy as np
import pynmea2
import xarray as xr
from ..echodata.convention import sonarnetcdf_1
from ..utils.coding import COMPRESSION_SETTINGS, set_time_encodings
from ..utils.prov import echopype_prov_attrs, source_files_vars
NMEA_SENTENCE_DEFAULT = ["GGA", "GLL", "RMC"]
class SetGroupsBase(abc.ABC):
"""Base class for saving groups to netcdf or zarr from echosounder data files."""
def __init__(
self,
parser_obj,
input_file,
xml_path,
output_path,
sonar_model=None,
engine="zarr",
compress=True,
overwrite=True,
params=None,
parsed2zarr_obj=None,
):
# parser object ParseEK60/ParseAZFP/etc...
self.parser_obj = parser_obj
# Used for when a sonar that is not AZFP/EK60/EK80 can still be saved
self.sonar_model = sonar_model
self.input_file = input_file
self.xml_path = xml_path
self.output_path = output_path
self.engine = engine
self.compress = compress
self.overwrite = overwrite
# parsed data written directly to zarr object
self.parsed2zarr_obj = parsed2zarr_obj
if not self.compress:
self.compression_settings = None
else:
self.compression_settings = COMPRESSION_SETTINGS[self.engine]
self._varattrs = sonarnetcdf_1.yaml_dict["variable_and_varattributes"]
# self._beamgroups must be a list of dicts, eg:
# [{"name":"Beam_group1", "descr":"contains complex backscatter data
# and other beam or channel-specific data."}]
self._beamgroups = []
# TODO: change the set_XXX methods to return a dataset to be saved
# in the overarching save method
def set_toplevel(self, sonar_model, date_created=None) -> xr.Dataset:
"""Set the top-level group."""
# Collect variables
tl_dict = {
"conventions": "CF-1.7, SONAR-netCDF4-1.0, ACDD-1.3",
"keywords": sonar_model,
"sonar_convention_authority": "ICES",
"sonar_convention_name": "SONAR-netCDF4",
"sonar_convention_version": "1.0",
"summary": "",
"title": "",
"date_created": np.datetime_as_string(date_created, "s") + "Z",
}
# Save
ds = xr.Dataset()
ds = ds.assign_attrs(tl_dict)
return ds
def set_provenance(self) -> xr.Dataset:
"""Set the Provenance group."""
prov_dict = echopype_prov_attrs(process_type="conversion")
files_vars = source_files_vars(self.input_file, self.xml_path)
if files_vars["meta_source_files_var"] is None:
source_vars = files_vars["source_files_var"]
else:
source_vars = {**files_vars["source_files_var"], **files_vars["meta_source_files_var"]}
ds = xr.Dataset(
data_vars=source_vars, coords=files_vars["source_files_coord"], attrs=prov_dict
)
return ds
@abc.abstractmethod
def set_env(self) -> xr.Dataset:
"""Set the Environment group."""
raise NotImplementedError
@abc.abstractmethod
def set_sonar(self) -> xr.Dataset:
"""Set the Sonar group."""
raise NotImplementedError
@abc.abstractmethod
def set_beam(self) -> xr.Dataset:
"""Set the /Sonar/Beam group."""
raise NotImplementedError
@abc.abstractmethod
def set_platform(self) -> xr.Dataset:
"""Set the Platform group."""
raise NotImplementedError
def set_nmea(self) -> xr.Dataset:
"""Set the Platform/NMEA group."""
# Save nan if nmea data is not encoded in the raw file
if len(self.parser_obj.nmea["nmea_string"]) != 0:
# Convert np.datetime64 numbers to seconds since 1900-01-01 00:00:00Z
# due to xarray.to_netcdf() error on encoding np.datetime64 objects directly
time = (
self.parser_obj.nmea["timestamp"] - np.datetime64("1900-01-01T00:00:00")
) / np.timedelta64(1, "s")
raw_nmea = self.parser_obj.nmea["nmea_string"]
else:
time = [np.nan]
raw_nmea = [np.nan]
ds = xr.Dataset(
{
"NMEA_datagram": (
["time1"],
raw_nmea,
{"long_name": "NMEA datagram"},
)
},
coords={
"time1": (
["time1"],
time,
{
"axis": "T",
"long_name": "Timestamps for NMEA datagrams",
"standard_name": "time",
"comment": "Time coordinate corresponding to NMEA sensor data.",
},
)
},
attrs={"description": "All NMEA sensor datagrams"},
)
return set_time_encodings(ds)
@abc.abstractmethod
def set_vendor(self) -> xr.Dataset:
"""Set the Vendor_specific group."""
raise NotImplementedError
# TODO: move this to be part of parser as it is not a "set" operation
def _extract_NMEA_latlon(self):
"""Get the lat and lon values from the raw nmea data"""
messages = [string[3:6] for string in self.parser_obj.nmea["nmea_string"]]
idx_loc = np.argwhere(np.isin(messages, NMEA_SENTENCE_DEFAULT)).squeeze()
if idx_loc.size == 1: # in case of only 1 matching message
idx_loc = np.expand_dims(idx_loc, axis=0)
nmea_msg = []
for x in idx_loc:
try:
nmea_msg.append(pynmea2.parse(self.parser_obj.nmea["nmea_string"][x]))
except (
pynmea2.ChecksumError,
pynmea2.SentenceTypeError,
AttributeError,
pynmea2.ParseError,
):
nmea_msg.append(None)
if nmea_msg:
lat, lon = [], []
for x in nmea_msg:
try:
lat.append(x.latitude if hasattr(x, "latitude") else np.nan)
except ValueError as ve:
lat.append(np.nan)
warnings.warn(
"At least one latitude entry is problematic and "
f"are assigned None in the converted data: {str(ve)}"
)
try:
lon.append(x.longitude if hasattr(x, "longitude") else np.nan)
except ValueError as ve:
lon.append(np.nan)
warnings.warn(
f"At least one longitude entry is problematic and "
f"are assigned None in the converted data: {str(ve)}"
)
else:
lat, lon = [np.nan], [np.nan]
msg_type = (
[x.sentence_type if hasattr(x, "sentence_type") else np.nan for x in nmea_msg]
if nmea_msg
else [np.nan]
)
time1 = (
(
np.array(self.parser_obj.nmea["timestamp"])[idx_loc]
- np.datetime64("1900-01-01T00:00:00")
)
/ np.timedelta64(1, "s")
if nmea_msg
else [np.nan]
)
return time1, msg_type, lat, lon
def _beam_groups_vars(self):
"""Stage beam_group coordinate and beam_group_descr variables sharing
a common dimension, beam_group, to be inserted in the Sonar group"""
beam_groups_vars = {
"beam_group_descr": (
["beam_group"],
[di["descr"] for di in self._beamgroups],
{"long_name": "Beam group description"},
),
}
beam_groups_coord = {
"beam_group": (
["beam_group"],
[di["name"] for di in self._beamgroups],
{"long_name": "Beam group name"},
),
}
return beam_groups_vars, beam_groups_coord
@staticmethod
def _add_beam_dim(ds: xr.Dataset, beam_only_names: Set[str], beam_ping_time_names: Set[str]):
"""
Adds ``beam`` as the last dimension to the appropriate
variables in ``Sonar/Beam_groupX`` groups when necessary.
Notes
-----
When expanding the dimension of a Dataarray, it is necessary
to copy the array (hence the .copy()). This allows the array
to be writable downstream (i.e. we can assign values to
certain indices).
To retain the attributes and encoding of ``beam``
it is necessary to use .assign_coords() with ``beam``
from ds.
"""
# variables to add beam to
add_beam_names = set(ds.variables).intersection(beam_only_names.union(beam_ping_time_names))
for var_name in add_beam_names:
if "beam" in ds.dims:
if "beam" not in ds[var_name].dims:
ds[var_name] = (
ds[var_name]
.expand_dims(dim={"beam": ds.beam}, axis=ds[var_name].ndim)
.assign_coords(beam=ds.beam)
.copy()
)
else:
# Add a single-value beam dimension and its attributes
ds[var_name] = (
ds[var_name]
.expand_dims(dim={"beam": np.array(["1"], dtype=str)}, axis=ds[var_name].ndim)
.copy()
)
ds[var_name].beam.attrs = sonarnetcdf_1.yaml_dict["variable_and_varattributes"][
"beam_coord_default"
]["beam"]
@staticmethod
def _add_ping_time_dim(
ds: xr.Dataset, beam_ping_time_names: Set[str], ping_time_only_names: Set[str]
):
"""
Adds ``ping_time`` as the last dimension to the appropriate
variables in ``Sonar/Beam_group1`` and ``Sonar/Beam_group2``
(when necessary).
Notes
-----
When expanding the dimension of a Dataarray, it is necessary
to copy the array (hence the .copy()). This allows the array
to be writable downstream (i.e. we can assign values to
certain indices).
To retain the attributes and encoding of ``ping_time``
it is necessary to use .assign_coords() with ``ping_time``
from ds.
"""
# variables to add ping_time to
add_ping_time_names = (
set(ds.variables).intersection(beam_ping_time_names).union(ping_time_only_names)
)
for var_name in add_ping_time_names:
ds[var_name] = (
ds[var_name]
.expand_dims(dim={"ping_time": ds.ping_time}, axis=ds[var_name].ndim)
.assign_coords(ping_time=ds.ping_time)
.copy()
)
def beam_groups_to_convention(
self,
ds: xr.Dataset,
beam_only_names: Set[str],
beam_ping_time_names: Set[str],
ping_time_only_names: Set[str],
):
"""
Manipulates variables in ``Sonar/Beam_groupX``
to adhere to SONAR-netCDF4 vers. 1 with respect
to the use of ``ping_time`` and ``beam`` dimensions.
This does several things:
1. Creates ``beam`` dimension and coordinate variable
when not present.
2. Adds ``beam`` dimension to several variables
when missing.
3. Adds ``ping_time`` dimension to several variables
when missing.
Parameters
----------
ds : xr.Dataset
Dataset corresponding to ``Beam_groupX``.
beam_only_names : Set[str]
Variables that need only the beam dimension added to them.
beam_ping_time_names : Set[str]
Variables that need beam and ping_time dimensions added to them.
ping_time_only_names : Set[str]
Variables that need only the ping_time dimension added to them.
"""
self._add_ping_time_dim(ds, beam_ping_time_names, ping_time_only_names)
self._add_beam_dim(ds, beam_only_names, beam_ping_time_names)
def _get_channel_ids(self, chan_str: np.ndarray) -> List[str]:
"""
Obtains the channel IDs associated with ``chan_str``.
Parameters
----------
chan_str : np.ndarray
A numpy array of strings corresponding to the
keys of ``config_datagram["transceivers"]``
Returns
-------
A list of strings representing the channel IDS
"""
if self.sonar_model in ["EK60", "ES70"]:
return [
self.parser_obj.config_datagram["transceivers"][int(i)]["channel_id"]
for i in chan_str
]
else:
return [
self.parser_obj.config_datagram["configuration"][i]["channel_id"] for i in chan_str
]
def _get_power_dataarray(self, zarr_path: str) -> xr.DataArray:
"""
Constructs a DataArray from a Dask array for the power
data.
Parameters
----------
zarr_path: str
Path to the zarr file that contain the power data
Returns
-------
DataArray named "backscatter_r" representing the
power data.
"""
# collect variables associated with the power data
power = dask.array.from_zarr(zarr_path, component="power/power")
pow_time_path = "power/" + self.parsed2zarr_obj.power_dims[0]
pow_chan_path = "power/" + self.parsed2zarr_obj.power_dims[1]
power_time = dask.array.from_zarr(zarr_path, component=pow_time_path).compute()
power_channel = dask.array.from_zarr(zarr_path, component=pow_chan_path).compute()
# obtain channel names for power data
pow_chan_names = self._get_channel_ids(power_channel)
backscatter_r = xr.DataArray(
data=power,
coords={
"ping_time": (
["ping_time"],
power_time,
self._varattrs["beam_coord_default"]["ping_time"],
),
"channel": (
["channel"],
pow_chan_names,
self._varattrs["beam_coord_default"]["channel"],
),
"range_sample": (
["range_sample"],
np.arange(power.shape[2]),
self._varattrs["beam_coord_default"]["range_sample"],
),
},
name="backscatter_r",
attrs={
"long_name": self._varattrs["beam_var_default"]["backscatter_r"]["long_name"],
"units": "dB",
},
)
return backscatter_r
def _get_angle_dataarrays(self, zarr_path: str) -> Tuple[xr.DataArray, xr.DataArray]:
"""
Constructs the DataArrays from Dask arrays associated
with the angle data.
Parameters
----------
zarr_path: str
Path to the zarr file that contains the angle data
Returns
-------
DataArrays named "angle_athwartship" and "angle_alongship",
respectively, representing the angle data.
"""
# collect variables associated with the angle data
angle_along = dask.array.from_zarr(zarr_path, component="angle/angle_alongship")
angle_athwart = dask.array.from_zarr(zarr_path, component="angle/angle_athwartship")
ang_time_path = "angle/" + self.parsed2zarr_obj.angle_dims[0]
ang_chan_path = "angle/" + self.parsed2zarr_obj.angle_dims[1]
angle_time = dask.array.from_zarr(zarr_path, component=ang_time_path).compute()
angle_channel = dask.array.from_zarr(zarr_path, component=ang_chan_path).compute()
# obtain channel names for angle data
ang_chan_names = self._get_channel_ids(angle_channel)
array_coords = {
"ping_time": (
["ping_time"],
angle_time,
self._varattrs["beam_coord_default"]["ping_time"],
),
"channel": (
["channel"],
ang_chan_names,
self._varattrs["beam_coord_default"]["channel"],
),
"range_sample": (
["range_sample"],
np.arange(angle_athwart.shape[2]),
self._varattrs["beam_coord_default"]["range_sample"],
),
}
angle_athwartship = xr.DataArray(
data=angle_athwart,
coords=array_coords,
name="angle_athwartship",
attrs={
"long_name": "electrical athwartship angle",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
+ "The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. " # noqa
),
},
)
angle_alongship = xr.DataArray(
data=angle_along,
coords=array_coords,
name="angle_alongship",
attrs={
"long_name": "electrical alongship angle",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
+ "The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. " # noqa
),
},
)
return angle_athwartship, angle_alongship
def _get_complex_dataarrays(self, zarr_path: str) -> Tuple[xr.DataArray, xr.DataArray]:
"""
Constructs the DataArrays from Dask arrays associated
with the complex data.
Parameters
----------
zarr_path: str
Path to the zarr file that contains the complex data
Returns
-------
DataArrays named "backscatter_r" and "backscatter_i",
respectively, representing the complex data.
"""
# collect variables associated with the complex data
complex_r = dask.array.from_zarr(zarr_path, component="complex/backscatter_r")
complex_i = dask.array.from_zarr(zarr_path, component="complex/backscatter_i")
comp_time_path = "complex/" + self.parsed2zarr_obj.complex_dims[0]
comp_chan_path = "complex/" + self.parsed2zarr_obj.complex_dims[1]
complex_time = dask.array.from_zarr(zarr_path, component=comp_time_path).compute()
complex_channel = dask.array.from_zarr(zarr_path, component=comp_chan_path).compute()
# obtain channel names for complex data
comp_chan_names = self._get_channel_ids(complex_channel)
array_coords = {
"ping_time": (
["ping_time"],
complex_time,
self._varattrs["beam_coord_default"]["ping_time"],
),
"channel": (
["channel"],
comp_chan_names,
self._varattrs["beam_coord_default"]["channel"],
),
"range_sample": (
["range_sample"],
np.arange(complex_r.shape[2]),
self._varattrs["beam_coord_default"]["range_sample"],
),
"beam": (
["beam"],
np.arange(start=1, stop=complex_r.shape[3] + 1).astype(str),
self._varattrs["beam_coord_default"]["beam"],
),
}
backscatter_r = xr.DataArray(
data=complex_r,
coords=array_coords,
name="backscatter_r",
attrs={
"long_name": self._varattrs["beam_var_default"]["backscatter_r"]["long_name"],
"units": "dB",
},
)
backscatter_i = xr.DataArray(
data=complex_i,
coords=array_coords,
name="backscatter_i",
attrs={
"long_name": self._varattrs["beam_var_default"]["backscatter_i"]["long_name"],
"units": "dB",
},
)
return backscatter_r, backscatter_i
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,819 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/clean/api.py | """
Functions for reducing variabilities in backscatter data.
"""
from ..utils.prov import add_processing_level, echopype_prov_attrs, insert_input_processing_level
from .noise_est import NoiseEst
def estimate_noise(ds_Sv, ping_num, range_sample_num, noise_max=None):
"""
Estimate background noise by computing mean calibrated power of a collection of pings.
See ``remove_noise`` for reference.
Parameters
----------
ds_Sv : xr.Dataset
dataset containing ``Sv`` and ``echo_range`` [m]
ping_num : int
number of pings to obtain noise estimates
range_sample_num : int
number of samples along the ``range_sample`` dimension to obtain noise estimates
noise_max : float
the upper limit for background noise expected under the operating conditions
Returns
-------
A DataArray containing noise estimated from the input ``ds_Sv``
"""
noise_obj = NoiseEst(ds_Sv=ds_Sv.copy(), ping_num=ping_num, range_sample_num=range_sample_num)
noise_obj.estimate_noise(noise_max=noise_max)
return noise_obj.Sv_noise
@add_processing_level("L*B")
def remove_noise(ds_Sv, ping_num, range_sample_num, noise_max=None, SNR_threshold=3):
"""
Remove noise by using estimates of background noise
from mean calibrated power of a collection of pings.
Reference: De Robertis & Higginbottom. 2007.
A post-processing technique to estimate the signal-to-noise ratio
and remove echosounder background noise.
ICES Journal of Marine Sciences 64(6): 1282–1291.
Parameters
----------
ds_Sv : xr.Dataset
dataset containing ``Sv`` and ``echo_range`` [m]
ping_num : int
number of pings to obtain noise estimates
range_sample_num : int
number of samples along the ``range_sample`` dimension to obtain noise estimates
noise_max : float
the upper limit for background noise expected under the operating conditions
SNR_threshold : float
acceptable signal-to-noise ratio, default to 3 dB
Returns
-------
The input dataset with additional variables, including
the corrected Sv (``Sv_corrected``) and the noise estimates (``Sv_noise``)
"""
noise_obj = NoiseEst(ds_Sv=ds_Sv.copy(), ping_num=ping_num, range_sample_num=range_sample_num)
noise_obj.remove_noise(noise_max=noise_max, SNR_threshold=SNR_threshold)
ds_Sv = noise_obj.ds_Sv
prov_dict = echopype_prov_attrs(process_type="processing")
prov_dict["processing_function"] = "clean.remove_noise"
ds_Sv = ds_Sv.assign_attrs(prov_dict)
# The output ds_Sv is built as a copy of the input ds_Sv, so the step below is
# not needed, strictly speaking. But doing makes the decorator function more generic
ds_Sv = insert_input_processing_level(ds_Sv, input_ds=ds_Sv)
return ds_Sv
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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,820 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/calibrate_ek.py | from typing import Dict
import numpy as np
import xarray as xr
from ..echodata import EchoData
from ..echodata.simrad import retrieve_correct_beam_group
from ..utils.log import _init_logger
from .cal_params import _get_interp_da, get_cal_params_EK
from .calibrate_base import CalibrateBase
from .ecs import conform_channel_order, ecs_ds2dict, ecs_ev2ep
from .ek80_complex import (
compress_pulse,
get_filter_coeff,
get_norm_fac,
get_tau_effective,
get_transmit_signal,
)
from .env_params import get_env_params_EK
from .range import compute_range_EK, range_mod_TVG_EK
logger = _init_logger(__name__)
class CalibrateEK(CalibrateBase):
def __init__(self, echodata: EchoData, env_params, cal_params, ecs_file, **kwargs):
super().__init__(echodata, env_params, cal_params, ecs_file)
self.ed_beam_group = None # will be assigned in child class
self.ed_beam_group = None # will be assigned in child class
def compute_echo_range(self, chan_sel: xr.DataArray = None):
"""
Compute echo range for EK echosounders.
Returns
-------
range_meter : xr.DataArray
range in units meter
"""
self.range_meter = compute_range_EK(
echodata=self.echodata,
env_params=self.env_params,
waveform_mode=self.waveform_mode,
encode_mode=self.encode_mode,
chan_sel=chan_sel,
)
def _cal_power_samples(self, cal_type: str) -> xr.Dataset:
"""Calibrate power data from EK60 and EK80.
Parameters
----------
cal_type: str
'Sv' for calculating volume backscattering strength, or
'TS' for calculating target strength
Returns
-------
xr.Dataset
The calibrated dataset containing Sv or TS
"""
# Select source of backscatter data
beam = self.echodata[self.ed_beam_group]
# Derived params
wavelength = self.env_params["sound_speed"] / beam["frequency_nominal"] # wavelength
# range_meter = self.range_meter
# TVG compensation with modified range
sound_speed = self.env_params["sound_speed"]
absorption = self.env_params["sound_absorption"]
tvg_mod_range = range_mod_TVG_EK(
self.echodata, self.ed_beam_group, self.range_meter, sound_speed
)
tvg_mod_range = tvg_mod_range.where(tvg_mod_range > 0, np.nan)
spreading_loss = 20 * np.log10(tvg_mod_range)
absorption_loss = 2 * absorption * tvg_mod_range
if cal_type == "Sv":
# Calc gain
CSv = (
10 * np.log10(beam["transmit_power"])
+ 2 * self.cal_params["gain_correction"]
+ self.cal_params["equivalent_beam_angle"]
+ 10
* np.log10(
wavelength**2
* beam["transmit_duration_nominal"]
* self.env_params["sound_speed"]
/ (32 * np.pi**2)
)
)
# Calibration and echo integration
out = (
beam["backscatter_r"] # has beam dim
+ spreading_loss
+ absorption_loss
- CSv
- 2 * self.cal_params["sa_correction"]
)
out.name = "Sv"
elif cal_type == "TS":
# Calc gain
CSp = (
10 * np.log10(beam["transmit_power"])
+ 2 * self.cal_params["gain_correction"]
+ 10 * np.log10(wavelength**2 / (16 * np.pi**2))
)
# Calibration and echo integration
out = beam["backscatter_r"] + spreading_loss * 2 + absorption_loss - CSp
out.name = "TS"
# Attach calculated range (with units meter) into data set
out = out.to_dataset()
out = out.merge(self.range_meter)
# Add frequency_nominal to data set
out["frequency_nominal"] = beam["frequency_nominal"]
# Add env and cal parameters
out = self._add_params_to_output(out)
# Remove time1 if exist as a coordinate
if "time1" in out.coords:
out = out.drop("time1")
return out
class CalibrateEK60(CalibrateEK):
def __init__(self, echodata: EchoData, env_params, cal_params, ecs_file, **kwargs):
super().__init__(echodata, env_params, cal_params, ecs_file)
# Set sonar_type and waveform/encode mode
self.sonar_type = "EK60"
# Set cal type
self.waveform_mode = "CW"
self.encode_mode = "power"
# Get the right ed_beam_group for CW power samples
self.ed_beam_group = retrieve_correct_beam_group(
echodata=self.echodata, waveform_mode=self.waveform_mode, encode_mode=self.encode_mode
)
# Set the channels to calibrate
# For EK60 this is all channels
self.chan_sel = self.echodata[self.ed_beam_group]["channel"]
beam = self.echodata[self.ed_beam_group]
# Convert env_params and cal_params if self.ecs_file exists
# Note a warning if thrown out in CalibrateBase.__init__
# to let user know cal_params and env_params are ignored if ecs_file is provided
if self.ecs_file is not None: # also means self.ecs_dict != {}
ds_env_tmp, ds_cal_tmp, _ = ecs_ev2ep(self.ecs_dict, "EK60")
self.cal_params = ecs_ds2dict(
conform_channel_order(ds_cal_tmp, beam["frequency_nominal"])
)
self.env_params = ecs_ds2dict(
conform_channel_order(ds_env_tmp, beam["frequency_nominal"])
)
# Regardless of the source cal and env params,
# go through the same sanitization and organization process
self.env_params = get_env_params_EK(
sonar_type=self.sonar_type,
beam=self.echodata[self.ed_beam_group],
env=self.echodata["Environment"],
user_dict=self.env_params,
)
self.cal_params = get_cal_params_EK(
waveform_mode=self.waveform_mode,
freq_center=beam["frequency_nominal"],
beam=beam,
vend=self.echodata["Vendor_specific"],
user_dict=self.cal_params,
sonar_type=self.sonar_type,
)
# Compute range
self.compute_echo_range()
def compute_Sv(self, **kwargs):
return self._cal_power_samples(cal_type="Sv")
def compute_TS(self, **kwargs):
return self._cal_power_samples(cal_type="TS")
class CalibrateEK80(CalibrateEK):
# Default EK80 params: these parameters are only recorded in later versions of EK80 software
EK80_params = {}
EK80_params["z_et"] = 75 # transducer impedance
EK80_params["z_er"] = 1000 # transceiver impedance
EK80_params["fs"] = { # default full sampling frequency [Hz]
"default": 1500000,
"GPT": 500000,
"SBT": 50000,
"WBAT": 1500000,
"WBT TUBE": 1500000,
"WBT MINI": 1500000,
"WBT": 1500000,
"WBT HP": 187500,
"WBT LF": 93750,
}
def __init__(
self,
echodata: EchoData,
env_params,
cal_params,
waveform_mode,
encode_mode,
ecs_file=None,
**kwargs,
):
super().__init__(echodata, env_params, cal_params, ecs_file)
# Set sonar_type
self.sonar_type = "EK80"
# The waveform and encode mode combination checked in calibrate/api.py::_compute_cal
# so just doing assignment here
self.waveform_mode = waveform_mode
self.encode_mode = encode_mode
self.echodata = echodata
# Get the right ed_beam_group given waveform and encode mode
self.ed_beam_group = retrieve_correct_beam_group(
echodata=self.echodata, waveform_mode=self.waveform_mode, encode_mode=self.encode_mode
)
# Select the channels to calibrate
if self.encode_mode == "power":
# Power sample only possible under CW mode,
# and all power samples will live in the same group
self.chan_sel = self.echodata[self.ed_beam_group]["channel"]
else:
# Complex samples can be CW or BB, so select based on waveform mode
chan_dict = self._get_chan_dict(self.echodata[self.ed_beam_group])
self.chan_sel = chan_dict[self.waveform_mode]
# Subset of the right Sonar/Beam_groupX group given the selected channels
beam = self.echodata[self.ed_beam_group].sel(channel=self.chan_sel)
# Use center frequency if in BB mode, else use nominal channel frequency
if self.waveform_mode == "BB":
# use true center frequency to interpolate for various cal params
self.freq_center = (
beam["transmit_frequency_start"] + beam["transmit_frequency_stop"]
).sel(channel=self.chan_sel) / 2
else:
# use nominal channel frequency for CW pulse
self.freq_center = beam["frequency_nominal"].sel(channel=self.chan_sel)
# Convert env_params and cal_params if self.ecs_file exists
# Note a warning if thrown out in CalibrateBase.__init__
# to let user know cal_params and env_params are ignored if ecs_file is provided
if self.ecs_file is not None: # also means self.ecs_dict != {}
ds_env, ds_cal_NB, ds_cal_BB = ecs_ev2ep(self.ecs_dict, "EK80")
self.env_params = ecs_ds2dict(
conform_channel_order(ds_env, beam["frequency_nominal"].sel(channel=self.chan_sel))
)
ds_cal_BB = conform_channel_order(
ds_cal_BB, beam["frequency_nominal"].sel(channel=self.chan_sel)
)
ds_cal_NB = self._scale_ecs_cal_params_NB(
conform_channel_order(
ds_cal_NB, beam["frequency_nominal"].sel(channel=self.chan_sel)
),
beam,
)
cal_params_dict = ecs_ds2dict(ds_cal_NB)
if ds_cal_BB is not None:
# get_cal_params_EK fill in empty params at param level, not channel level,
# so need to do freq-dep interpolation here
self.cal_params = self._assimilate_ecs_cal_params(cal_params_dict, ds_cal_BB)
else:
self.cal_params = cal_params_dict
# Get env_params: depends on waveform mode
self.env_params = get_env_params_EK(
sonar_type=self.sonar_type,
beam=beam,
env=self.echodata["Environment"],
user_dict=self.env_params,
freq=self.freq_center,
)
# Get cal_params: depends on waveform and encode mode
self.cal_params = get_cal_params_EK(
waveform_mode=self.waveform_mode,
freq_center=self.freq_center,
beam=beam, # already subset above
vend=self.echodata["Vendor_specific"].sel(channel=self.chan_sel),
user_dict=self.cal_params,
sonar_type="EK80",
)
# Compute echo range in meters
self.compute_echo_range(chan_sel=self.chan_sel)
@staticmethod
def _get_chan_dict(beam: xr.Dataset) -> Dict:
"""
Build dict to select BB and CW channels from complex samples where data
from both waveform modes may co-exist.
"""
# Use center frequency for each ping to select BB or CW channels
# when all samples are encoded as complex samples
if not np.all(beam["transmit_type"] == "CW"):
# At least 1 BB ping exists -- this is analogous to what we had from before
# Before: when at least 1 BB ping exists, frequency_start and frequency_end will exist
# assume transmit_type identical for all pings in a channel
first_ping_transmit_type = (
beam["transmit_type"].isel(ping_time=0).drop_vars("ping_time")
) # noqa
return {
# For BB: Keep only non-CW channels (LFM or FMD) based on transmit_type
"BB": first_ping_transmit_type.where(
first_ping_transmit_type != "CW", drop=True
).channel, # noqa
# For CW: Keep only CW channels based on transmit_type
"CW": first_ping_transmit_type.where(
first_ping_transmit_type == "CW", drop=True
).channel, # noqa
}
else:
# All channels are CW
return {"BB": None, "CW": beam.channel}
def _scale_ecs_cal_params_NB(self, ds_cal_NB: xr.Dataset, beam: xr.Dataset) -> xr.Dataset:
"""
Scale narrowband parameters based on center frequency of each ping
with respect to channel nominal frequency.
"""
for p in ds_cal_NB:
if p in ["angle_sensitivity_alongship", "angle_sensitivity_athwartship"]:
ds_cal_NB[p] = ds_cal_NB[p] * self.freq_center / beam["frequency_nominal"]
elif p in ["beamwidth_alongship", "beamwidth_athwartship"]:
ds_cal_NB[p] = ds_cal_NB[p] * beam["frequency_nominal"] / self.freq_center
elif p == "equivalent_beam_angle":
ds_cal_NB[p] = ds_cal_NB[p] + 20 * np.log10(
beam["frequency_nominal"] / self.freq_center
)
return ds_cal_NB
def _assimilate_ecs_cal_params(self, cal_params_dict: Dict, ds_cal_BB: xr.Dataset):
"""
Combine narrowband and broadband parameters derived from ECS.
"""
if ds_cal_BB is not None:
ds_cal_BB = ds_cal_BB.rename({"channel": "cal_channel_id"})
for p in ds_cal_BB.data_vars:
# For parameters where there is frequency-dependent values,
# the corresponding narrowband (CW mode) values should exist for all channels
if not np.all(
[
ch in cal_params_dict[p]["channel"].values
for ch in ds_cal_BB["cal_channel_id"].values
]
):
raise ValueError(
f"Narrowband (CW mode) parameter {p} should exist "
"for all channels with frequency-dependent parameter values."
)
# Assemble parameter data array with all channels
# Either interpolate or pull from narrowband input
# The ping_time dimension has to persist for BB case,
# because center frequency may change across ping
if "ping_time" in cal_params_dict[p].coords:
ds_cal_BB[p] = _get_interp_da(
da_param=ds_cal_BB[p], # freq-dep xr.DataArray
freq_center=self.freq_center,
alternative=cal_params_dict[p],
)
else:
ds_cal_BB[p] = _get_interp_da(
da_param=ds_cal_BB[p], # freq-dep xr.DataArray
freq_center=self.freq_center,
alternative=cal_params_dict[p].expand_dims(
dim={"ping_time": self.freq_center["ping_time"].size}, axis=1
),
)
# Keep only 'channel' and 'ping_time' coorindates
ds_cal_BB = ds_cal_BB.drop_dims(["cal_frequency", "cal_channel_id"])
# Substitute params in narrowband dict
return dict(cal_params_dict, **ecs_ds2dict(ds_cal_BB))
else:
# Do nothing if ds_cal_BB is None
return cal_params_dict
def _get_power_from_complex(
self,
beam: xr.Dataset,
chirp: Dict,
z_et: float,
z_er: float,
) -> xr.DataArray:
"""
Get power from complex samples.
Parameters
----------
beam : xr.Dataset
EchoData["Sonar/Beam_group1"] with selected channel subset
chirp : dict
a dictionary containing transmit chirp for BB channels
z_et : float
impedance of transducer [ohm]
z_er : float
impedance of transceiver [ohm]
Returns
-------
prx : xr.DataArray
Power computed from complex samples
"""
def _get_prx(sig):
return (
beam["beam"].size # number of transducer sectors
* np.abs(sig.mean(dim="beam")) ** 2
/ (2 * np.sqrt(2)) ** 2
* (np.abs(z_er + z_et) / z_er) ** 2
/ z_et
)
# Compute power
if self.waveform_mode == "BB":
pc = compress_pulse(
backscatter=beam["backscatter_r"] + 1j * beam["backscatter_i"], chirp=chirp
) # has beam dim
pc = pc / get_norm_fac(chirp=chirp) # normalization for each channel
prx = _get_prx(pc) # ensure prx is xr.DataArray
else:
bs_cw = beam["backscatter_r"] + 1j * beam["backscatter_i"]
prx = _get_prx(bs_cw)
prx.name = "received_power"
return prx
def _get_B_theta_phi_m(self):
"""
Get transceiver gain compensation for BB mode.
Source: https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf/blob/abd01f9c271bb2dbe558c80893dbd7eb0d06fe38/Core/EK80DataContainer.py#L261-L273 # noqa
From conversation with Lars Andersen, this correction is based on a longstanding
empirical formula used for fitting beampattern during calibration, based on
physically meaningful parameters such as the angle offset and beamwidth.
"""
fac_along = (
np.abs(-self.cal_params["angle_offset_alongship"])
/ (self.cal_params["beamwidth_alongship"] / 2)
) ** 2
fac_athwart = (
np.abs(-self.cal_params["angle_offset_athwartship"])
/ (self.cal_params["beamwidth_athwartship"] / 2)
) ** 2
B_theta_phi_m = 0.5 * 6.0206 * (fac_along + fac_athwart - 0.18 * fac_along * fac_athwart)
return B_theta_phi_m
def _cal_complex_samples(self, cal_type: str) -> xr.Dataset:
"""Calibrate complex data from EK80.
Parameters
----------
cal_type : str
'Sv' for calculating volume backscattering strength, or
'TS' for calculating target strength
Returns
-------
xr.Dataset
The calibrated dataset containing Sv or TS
"""
# Select source of backscatter data
beam = self.echodata[self.ed_beam_group].sel(channel=self.chan_sel)
vend = self.echodata["Vendor_specific"].sel(channel=self.chan_sel)
# Get transmit signal
tx_coeff = get_filter_coeff(vend)
fs = self.cal_params["receiver_sampling_frequency"]
# Switch to use Andersen implementation for transmit chirp starting v0.6.4
tx, tx_time = get_transmit_signal(beam, tx_coeff, self.waveform_mode, fs)
# Params to clarity in use below
z_er = self.cal_params["impedance_transceiver"]
z_et = self.cal_params["impedance_transducer"]
gain = self.cal_params["gain_correction"]
# Transceiver gain compensation for BB mode
if self.waveform_mode == "BB":
gain = gain - self._get_B_theta_phi_m()
absorption = self.env_params["sound_absorption"]
range_meter = self.range_meter
sound_speed = self.env_params["sound_speed"]
wavelength = sound_speed / self.freq_center
transmit_power = beam["transmit_power"]
# TVG compensation with modified range
tvg_mod_range = range_mod_TVG_EK(
self.echodata, self.ed_beam_group, range_meter, sound_speed
)
tvg_mod_range = tvg_mod_range.where(tvg_mod_range > 0, np.nan)
spreading_loss = 20 * np.log10(tvg_mod_range)
absorption_loss = 2 * absorption * tvg_mod_range
# Get power from complex samples
prx = self._get_power_from_complex(beam=beam, chirp=tx, z_et=z_et, z_er=z_er)
prx = prx.where(prx > 0, np.nan)
# Compute based on cal_type
if cal_type == "Sv":
# Effective pulse length
# compute first assuming all channels are not GPT
tau_effective = get_tau_effective(
ytx_dict=tx,
fs_deci_dict={k: 1 / np.diff(v[:2]) for (k, v) in tx_time.items()}, # decimated fs
waveform_mode=self.waveform_mode,
channel=self.chan_sel,
ping_time=beam["ping_time"],
)
# Use pulse_duration in place of tau_effective for GPT channels
# below assumesthat all transmit parameters are identical
# and needs to be changed when allowing transmit parameters to vary by ping
ch_GPT = vend["transceiver_type"] == "GPT"
tau_effective[ch_GPT] = beam["transmit_duration_nominal"][ch_GPT].isel(ping_time=0)
# equivalent_beam_angle
# TODO: THIS ONE CARRIES THE BEAM DIMENSION AROUND
psifc = self.cal_params["equivalent_beam_angle"]
out = (
10 * np.log10(prx)
+ spreading_loss
+ absorption_loss
- 10 * np.log10(wavelength**2 * transmit_power * sound_speed / (32 * np.pi**2))
- 2 * gain
- 10 * np.log10(tau_effective)
- psifc
)
# Correct for sa_correction if CW mode
if self.waveform_mode == "CW":
out = out - 2 * self.cal_params["sa_correction"]
out.name = "Sv"
# out = out.rename_vars({list(out.data_vars.keys())[0]: "Sv"})
elif cal_type == "TS":
out = (
10 * np.log10(prx)
+ 2 * spreading_loss
+ absorption_loss
- 10 * np.log10(wavelength**2 * transmit_power / (16 * np.pi**2))
- 2 * gain
)
out.name = "TS"
# Attach calculated range (with units meter) into data set
out = out.to_dataset().merge(range_meter)
# Add frequency_nominal to data set
out["frequency_nominal"] = beam["frequency_nominal"]
# Add env and cal parameters
out = self._add_params_to_output(out)
return out
def _compute_cal(self, cal_type) -> xr.Dataset:
"""
Private method to compute Sv or TS from EK80 data, called by compute_Sv or compute_TS.
Parameters
----------
cal_type : str
'Sv' for calculating volume backscattering strength, or
'TS' for calculating target strength
Returns
-------
xr.Dataset
An xarray Dataset containing either Sv or TS.
"""
# Set flag_complex: True-complex cal, False-power cal
flag_complex = (
True if self.waveform_mode == "BB" or self.encode_mode == "complex" else False
)
if flag_complex:
# Complex samples can be BB or CW
ds_cal = self._cal_complex_samples(cal_type=cal_type)
else:
# Power samples only make sense for CW mode data
ds_cal = self._cal_power_samples(cal_type=cal_type)
return ds_cal
def compute_Sv(self):
"""Compute volume backscattering strength (Sv).
Returns
-------
Sv : xr.DataSet
A DataSet containing volume backscattering strength (``Sv``)
and the corresponding range (``echo_range``) in units meter.
"""
return self._compute_cal(cal_type="Sv")
def compute_TS(self):
"""Compute target strength (TS).
Returns
-------
TS : xr.DataSet
A DataSet containing target strength (``TS``)
and the corresponding range (``echo_range``) in units meter.
"""
return self._compute_cal(cal_type="TS")
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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,821 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/cal_params.py | from typing import Dict, List, Literal, Union
import numpy as np
import xarray as xr
CAL_PARAMS = {
"EK60": (
"sa_correction",
"gain_correction",
"equivalent_beam_angle",
"angle_offset_alongship",
"angle_offset_athwartship",
"angle_sensitivity_alongship",
"angle_sensitivity_athwartship",
"beamwidth_alongship",
"beamwidth_athwartship",
),
"EK80": (
"sa_correction",
"gain_correction",
"equivalent_beam_angle",
"angle_offset_alongship",
"angle_offset_athwartship",
"angle_sensitivity_alongship",
"angle_sensitivity_athwartship",
"beamwidth_alongship",
"beamwidth_athwartship",
"impedance_transducer", # z_et
"impedance_transceiver", # z_er
"receiver_sampling_frequency",
),
"AZFP": ("EL", "DS", "TVR", "VTX", "equivalent_beam_angle", "Sv_offset"),
}
EK80_DEFAULT_PARAMS = {
"impedance_transducer": 75,
"impedance_transceiver": 1000,
"receiver_sampling_frequency": { # default full sampling frequency [Hz]
"default": 1500000,
"GPT": 500000,
"SBT": 50000,
"WBAT": 1500000,
"WBT TUBE": 1500000,
"WBT MINI": 1500000,
"WBT": 1500000,
"WBT HP": 187500,
"WBT LF": 93750,
},
}
def param2da(p_val: Union[int, float, list], channel: Union[list, xr.DataArray]) -> xr.DataArray:
"""
Organize individual parameter in scalar or list to xr.DataArray with channel coordinate.
Parameters
----------
p_val : int, float, or list
A scalar or list holding calibration params for one or more channels.
Each param has to be a scalar.
channel : list or xr.DataArray
Values to use for the output channel coordinate
Returns
-------
xr.DataArray
A data array with channel coordinate
"""
# TODO: allow passing in np.array as dict values to assemble a frequency-dependent cal da
if not isinstance(p_val, (int, float, list)):
raise ValueError("'p_val' needs to be one of type int, float, or list")
if isinstance(p_val, list):
# Check length if p_val a list
if len(p_val) != len(channel):
raise ValueError("The lengths of 'p_val' and 'channel' should be identical")
return xr.DataArray(p_val, dims=["channel"], coords={"channel": channel})
else:
# if scalar, make a list to form data array
return xr.DataArray([p_val] * len(channel), dims=["channel"], coords={"channel": channel})
def sanitize_user_cal_dict(
sonar_type: Literal["EK60", "EK80", "AZFP"],
user_dict: Dict[str, Union[int, float, list, xr.DataArray]],
channel: Union[List, xr.DataArray],
) -> Dict[str, Union[int, float, xr.DataArray]]:
"""
Creates a blueprint for ``cal_params`` dictionary and
check the format/organize user-provided parameters.
Parameters
----------
sonar_type : str
Type of sonar, one of "EK60", "EK80", or "AZFP"
user_dict : dict
A dictionary containing user input calibration parameters
as {parameter name: parameter value}.
Parameter value has to be a scalar (int or float) or an ``xr.DataArray``.
If parameter value is an ``xr.DataArray``, it has to either have 'channel' as a coordinate
or have both ``cal_channel_id`` and ``cal_frequency`` as coordinates.
channel : list or xr.DataArray
A list of channels to be calibrated.
For EK80 data, this list has to corresponds with the subset of channels
selected based on waveform_mode and encode_mode
"""
# Check sonar type
if sonar_type not in ["EK60", "EK80", "AZFP"]:
raise ValueError("'sonar_type' has to be one of: 'EK60', 'EK80', or 'AZFP'")
# Make channel a sorted list
if not isinstance(channel, (list, xr.DataArray)):
raise ValueError("'channel' has to be a list or an xr.DataArray")
if isinstance(channel, xr.DataArray):
channel_sorted = sorted(channel.values)
else:
channel_sorted = sorted(channel)
# Screen parameters: only retain those defined in CAL_PARAMS
# -- transform params in scalar or list to xr.DataArray
# -- directly pass through those that are xr.DataArray and pass the check for coordinates
out_dict = dict.fromkeys(CAL_PARAMS[sonar_type])
for p_name, p_val in user_dict.items():
if p_name in out_dict:
# if p_val an xr.DataArray, check existence and coordinates
if isinstance(p_val, xr.DataArray):
# if 'channel' is a coordinate, it has to match that of the data
if "channel" in p_val.coords:
if not (sorted(p_val.coords["channel"].values) == channel_sorted):
raise ValueError(
f"The 'channel' coordinate of {p_name} has to match "
"that of the data to be calibrated"
)
elif "cal_channel_id" in p_val.coords and "cal_frequency" in p_val.coords:
if not (sorted(p_val.coords["cal_channel_id"].values) == channel_sorted):
raise ValueError(
f"The 'cal_channel_id' coordinate of {p_name} has to match "
"that of the data to be calibrated"
)
else:
raise ValueError(
f"{p_name} has to either have 'channel' as a coordinate "
"or have both 'cal_channel_id' and 'cal_frequency' as coordinates"
)
out_dict[p_name] = p_val
# If p_val a scalar or list, make it xr.DataArray
elif isinstance(p_val, (int, float, list)):
# check for list dimension happens within param2da()
out_dict[p_name] = param2da(p_val, channel)
# p_val has to be one of int, float, xr.DataArray
else:
raise ValueError(f"{p_name} has to be a scalar, list, or an xr.DataArray")
# TODO: Consider pre-sort the param xr.DataArray?
return out_dict
def _get_interp_da(
da_param: Union[None, xr.DataArray],
freq_center: xr.DataArray,
alternative: Union[int, float, xr.DataArray],
BB_factor: float = 1,
) -> xr.DataArray:
"""
Get interpolated xr.DataArray aligned with the channel coordinate.
Interpolation at freq_center when da_param contains frequency-dependent xr.DataArray.
When da_param is None or does not contain frequency-dependent xr.DataArray,
the alternative (a const or an xr.DataArray with coordinate channel) is used.
Parameters
----------
da_param : xr.DataArray or None
A data array from the Vendor group or user dict with freq-dependent param values
freq_center : xr.DataArray
Center frequency (BB) or nominal frequency (CW)
alternative : xr.DataArray or int or float
Alternative for when freq-dep values do not exist
BB_factor : float
scaling factor due to BB transmit signal with different center frequency
with respect to nominal channel frequency;
only applies when ``alternative`` from the Sonar/Beam_groupX group is used
for params ``angle_sensitivity_alongship/athwartship`` and
``beamwidth_alongship/athwartship`` (``see get_cal_params_EK`` for detail)
Returns
-------
xr.DataArray
Data array aligned with the channel coordinate.
Note
----
``da_param`` is always an xr.DataArray from the Vendor-specific group.
It is possible that only a subset of the channels have frequency-dependent parameter values.
The output xr.DataArray here is constructed channel-by-channel to allow for this flexibility.
``alternative`` can be one of the following:
- scalar (int or float): this is the case for impedance_transducer
- xr.DataArray with coordinates channel, ping_time, and beam:
this is the case for parameters angle_offset_alongship, angle_offset_athwartship,
beamwidth_alongship, beamwidth_athwartship
- xr.DataArray with coordinates channel, ping_time:
this is the case for sa_correction and gain_correction,
which will be direct output of get_vend_cal_params_power()
"""
param = []
for ch_id in freq_center["channel"].values:
# if frequency-dependent param exists as a data array with desired channel
if (
da_param is not None
and "cal_channel_id" in da_param.coords
and ch_id in da_param["cal_channel_id"]
):
# interp variable has ping_time dimension from freq_center
param.append(
da_param.sel(cal_channel_id=ch_id)
.interp(cal_frequency=freq_center.sel(channel=ch_id))
.data
)
# if no frequency-dependent param exists, use alternative
else:
BB_factor_ch = (
BB_factor.sel(channel=ch_id) if isinstance(BB_factor, xr.DataArray) else BB_factor
)
if isinstance(alternative, xr.DataArray):
alt = (alternative.sel(channel=ch_id) * BB_factor_ch).data.squeeze()
elif isinstance(alternative, (int, float)):
alt = (
np.array([alternative] * freq_center.sel(channel=ch_id).size).squeeze()
* BB_factor_ch
)
else:
raise ValueError("'alternative' has to be of the type int, float, or xr.DataArray")
if alt.size == 1 and "ping_time" in freq_center.coords:
# expand to size of ping_time coordinate
alt = np.array([alt] * freq_center.sel(channel=ch_id).size)
param.append(alt)
param = np.array(param)
if "ping_time" in freq_center.coords:
if len(param.shape) == 1: # this means param has only the channel but not the ping_time dim
param = np.expand_dims(param, axis=1)
return xr.DataArray(
param,
dims=["channel", "ping_time"],
coords={"channel": freq_center["channel"], "ping_time": freq_center["ping_time"]},
)
else:
return xr.DataArray(param, dims=["channel"], coords={"channel": freq_center["channel"]})
def get_vend_cal_params_power(beam: xr.Dataset, vend: xr.Dataset, param: str) -> xr.DataArray:
"""
Get cal parameters stored in the Vendor_specific group
by matching the transmit_duration_nominal with allowable pulse_length.
Parameters
----------
beam : xr.Dataset
A subset of Sonar/Beam_groupX that contains only the channels specified for calibration
vend : xr.Dataset
A subset of Vendor_specific that contains only the channels specified for calibration
param : str {"sa_correction", "gain_correction"}
Name of parameter to retrieve
Returns
-------
An xr.DataArray containing the matched ``param``
"""
# Check parameter is among those allowed
if param not in ["sa_correction", "gain_correction"]:
raise ValueError(f"Unknown parameter {param}")
# Check parameter exists
if param not in vend:
raise ValueError(f"{param} does not exist in the Vendor_specific group!")
# Find idx to select the corresponding param value
# by matching beam["transmit_duration_nominal"] with ds_vend["pulse_length"]
transmit_isnull = beam["transmit_duration_nominal"].isnull()
idxmin = np.abs(beam["transmit_duration_nominal"] - vend["pulse_length"]).idxmin(
dim="pulse_length_bin"
)
# fill nan position with 0 (will remove before return)
# and convert to int for indexing
idxmin = idxmin.where(~transmit_isnull, 0).astype(int)
# Get param dataarray into correct shape
da_param = (
vend[param]
.expand_dims(dim={"ping_time": idxmin["ping_time"]}) # expand dims for direct indexing
.sortby(idxmin.channel) # sortby in case channel sequence differs in vend and beam
)
# Select corresponding index and clean up the original nan elements
da_param = da_param.sel(pulse_length_bin=idxmin, drop=True)
# Set the nan elements back to nan.
# Doing the `.where` will result in float64,
# which is fine since we're dealing with nan
da_param = da_param.where(~transmit_isnull, np.nan)
# Clean up for leftover plb variable
# if exists
plb_var = "pulse_length_bin"
if plb_var in da_param.coords:
da_param = da_param.drop(plb_var)
return da_param
def get_cal_params_AZFP(beam: xr.DataArray, vend: xr.DataArray, user_dict: dict) -> dict:
"""
Get cal params using user inputs or values from data file.
Parameters
----------
beam : xr.Dataset
A subset of Sonar/Beam_groupX that contains only the channels to be calibrated
vend : xr.Dataset
A subset of Vendor_specific that contains only the channels to be calibrated
user_dict : dict
A dictionary containing user-defined calibration parameters.
The user-defined calibration parameters will overwrite values in the data file.
Returns
-------
A dictionary containing the calibration parameters for the AZFP echosounder
"""
# Use sanitized user dict as blueprint
# out_dict contains only and all of the allowable cal params
out_dict = sanitize_user_cal_dict(
user_dict=user_dict, channel=beam["channel"], sonar_type="AZFP"
)
# Only fill in params that are None
for p, v in out_dict.items():
if v is None:
# Params from Sonar/Beam_group1
if p == "equivalent_beam_angle":
out_dict[p] = beam[p] # has only channel dim
# Params from Vendor_specific group
elif p in ["EL", "DS", "TVR", "VTX", "Sv_offset"]:
out_dict[p] = vend[p] # these params only have the channel dimension
return out_dict
def get_cal_params_EK(
waveform_mode: Literal["CW", "BB"],
freq_center: xr.DataArray,
beam: xr.Dataset,
vend: xr.Dataset,
user_dict: Dict[str, Union[int, float, xr.DataArray]],
default_params: Dict[str, Union[int, float]] = EK80_DEFAULT_PARAMS,
sonar_type: str = "EK80",
) -> Dict:
"""
Get cal parameters from user input, data file, or a set of default values.
Parameters
----------
waveform_mode : str
Transmit waveform mode, either "CW" or "BB"
freq_center : xr.DataArray
Center frequency (BB mode) or nominal frequency (CW mode)
beam : xr.Dataset
A subset of Sonar/Beam_groupX that contains only the channels to be calibrated
vend : xr.Dataset
A subset of Vendor_specific that contains only the channels to be calibrated
user_dict : dict
A dictionary containing user-defined parameters.
User-defined parameters take precedance over values in the data file or in default dict.
default_params : dict
A dictionary containing default parameters
sonar_type : str
Type of EK sonar, either "EK60" or "EK80"
"""
if not isinstance(waveform_mode, str):
raise TypeError("waveform_mode is not type string")
elif waveform_mode not in ["CW", "BB"]:
raise ValueError("waveform_mode must be 'CW' or 'BB'")
# Private function to get fs
def _get_fs():
# If receiver_sampling_frequency recorded, use it
if "receiver_sampling_frequency" in vend:
return vend["receiver_sampling_frequency"]
else:
# If receiver_sampling_frequency not recorded, use default value
# loop through channel since transceiver can vary
fs = []
for ch in vend["channel"]:
tcvr_type = vend["transceiver_type"].sel(channel=ch).data.tolist().upper()
fs.append(default_params["receiver_sampling_frequency"][tcvr_type])
return xr.DataArray(fs, dims=["channel"], coords={"channel": vend["channel"]})
# Mapping between desired param name with Beam group data variable name
PARAM_BEAM_NAME_MAP = {
"angle_offset_alongship": "angle_offset_alongship",
"angle_offset_athwartship": "angle_offset_athwartship",
"angle_sensitivity_alongship": "angle_sensitivity_alongship",
"angle_sensitivity_athwartship": "angle_sensitivity_athwartship",
"beamwidth_alongship": "beamwidth_twoway_alongship",
"beamwidth_athwartship": "beamwidth_twoway_athwartship",
"equivalent_beam_angle": "equivalent_beam_angle",
}
if waveform_mode == "BB":
# for BB data equivalent_beam_angle needs to be scaled wrt freq_center
PARAM_BEAM_NAME_MAP.pop("equivalent_beam_angle")
# Use sanitized user dict as blueprint
# out_dict contains only and all of the allowable cal params
out_dict = sanitize_user_cal_dict(
user_dict=user_dict, channel=beam["channel"], sonar_type=sonar_type
)
# Interpolate user-input params that contain freq-dependent info
# ie those that has coordinate combination (cal_channel_id, cal_frequency)
# TODO: this will need to change for computing frequency-dependent TS
for p, v in out_dict.items():
if v is not None:
if "cal_channel_id" in v.coords:
out_dict[p] = _get_interp_da(v, freq_center, np.nan)
# Only fill in params that are None
for p, v in out_dict.items():
if v is None:
# Those without CW or BB complications
if p == "sa_correction": # pull from data file
out_dict[p] = get_vend_cal_params_power(beam=beam, vend=vend, param=p)
elif p == "impedance_transceiver": # from data file or default dict
out_dict[p] = default_params[p] if p not in vend else vend["impedance_transceiver"]
elif p == "receiver_sampling_frequency": # from data file or default_params
out_dict[p] = _get_fs()
else:
# CW: params do not require interpolation, except for impedance_transducer
if waveform_mode == "CW":
if p in PARAM_BEAM_NAME_MAP.keys():
# pull from data file, these should always exist
out_dict[p] = beam[PARAM_BEAM_NAME_MAP[p]]
elif p == "gain_correction":
# pull from data file narrowband table
out_dict[p] = get_vend_cal_params_power(beam=beam, vend=vend, param=p)
elif p == "impedance_transducer":
# assemble each channel from data file or default dict
out_dict[p] = _get_interp_da(
da_param=None if p not in vend else vend[p],
freq_center=freq_center,
alternative=default_params[p], # pull from default dict
)
else:
raise ValueError(f"{p} not in the defined set of calibration parameters.")
# BB mode: params require interpolation
else:
# interpolate for center frequency or use CW values
if p in PARAM_BEAM_NAME_MAP.keys():
# only scale these params if alternative is used
if p in [
"angle_sensitivity_alongship",
"angle_sensitivity_athwartship",
]:
BB_factor = freq_center / beam["frequency_nominal"]
elif p in [
"beamwidth_alongship",
"beamwidth_athwartship",
]:
BB_factor = beam["frequency_nominal"] / freq_center
else:
BB_factor = 1
p_beam = PARAM_BEAM_NAME_MAP[p] # Beam_groupX data variable name
out_dict[p] = _get_interp_da(
da_param=None if p not in vend else vend[p],
freq_center=freq_center,
alternative=beam[p_beam], # these should always exist
BB_factor=BB_factor,
)
elif p == "equivalent_beam_angle":
# scaled according to frequency ratio
out_dict[p] = beam[p] + 20 * np.log10(
beam["frequency_nominal"] / freq_center
)
elif p == "gain_correction":
# interpolate or pull from narrowband table
out_dict[p] = _get_interp_da(
da_param=None
if "gain" not in vend
else vend["gain"], # freq-dep values
freq_center=freq_center,
alternative=get_vend_cal_params_power(beam=beam, vend=vend, param=p),
)
elif p == "impedance_transducer":
out_dict[p] = _get_interp_da(
da_param=None if p not in vend else vend[p],
freq_center=freq_center,
alternative=default_params[p], # pull from default dict
)
else:
raise ValueError(f"{p} not in the defined set of calibration parameters.")
return out_dict
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,822 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/calibrate_base.py | import abc
from ..echodata import EchoData
from ..utils.log import _init_logger
from .ecs import ECSParser
logger = _init_logger(__name__)
class CalibrateBase(abc.ABC):
"""Class to handle calibration for all sonar models."""
def __init__(self, echodata: EchoData, env_params=None, cal_params=None, ecs_file=None):
self.echodata = echodata
self.sonar_type = None
self.ecs_file = ecs_file
self.ecs_dict = {}
# Set ECS to overwrite user-provided dict
if self.ecs_file is not None:
if env_params is not None or cal_params is not None:
logger.warning(
"The ECS file takes precedence when it is provided. "
"Parameter values provided in 'env_params' and 'cal_params' will not be used!"
)
# Parse ECS file to a dict
ecs = ECSParser(self.ecs_file)
ecs.parse()
self.ecs_dict = ecs.get_cal_params() # apply ECS hierarchy
self.env_params = {}
self.cal_params = {}
else:
if env_params is None:
self.env_params = {}
elif isinstance(env_params, dict):
self.env_params = env_params
else:
raise ValueError("'env_params' has to be None or a dict")
if cal_params is None:
self.cal_params = {}
elif isinstance(cal_params, dict):
self.cal_params = cal_params
else:
raise ValueError("'cal_params' has to be None or a dict")
# range_meter is computed in compute_Sv/TS in child class
self.range_meter = None
@abc.abstractmethod
def compute_echo_range(self, **kwargs):
"""Calculate range (``echo_range``) in units meter.
Returns
-------
range_meter : xr.DataArray
range in units meter
"""
pass
@abc.abstractmethod
def _cal_power_samples(self, cal_type, **kwargs):
"""Calibrate power data for EK60, EK80, and AZFP.
Parameters
----------
cal_type : str
'Sv' for calculating volume backscattering strength, or
'TS' for calculating target strength
"""
pass
@abc.abstractmethod
def compute_Sv(self, **kwargs):
pass
@abc.abstractmethod
def compute_TS(self, **kwargs):
pass
def _add_params_to_output(self, ds_out):
"""Add all cal and env parameters to output Sv dataset."""
# Add env_params
for key, val in self.env_params.items():
ds_out[key] = val
# Add cal_params
for key, val in self.cal_params.items():
ds_out[key] = val
return ds_out
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,823 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/commongrid/nasc.py | """
An overhaul is required for the below compute_NASC implementation, to:
- increase the computational efficiency
- debug the current code of any discrepancy against Echoview implementation
- potentially provide an alternative based on ping-by-ping Sv
This script contains functions used by commongrid.compute_NASC,
but a subset of these overlap with operations needed
for commongrid.compute_MVBS and clean.estimate_noise.
The compute_MVBS and remove_noise code needs to be refactored,
and the compute_NASC needs to be optimized.
The plan is to create a common util set of functions for use in
these functions in an upcoming release.
"""
import numpy as np
import xarray as xr
from geopy import distance
def check_identical_depth(ds_ch):
"""
Check if all pings have the same depth vector.
"""
# Depth vector are identical for all pings, if:
# - the number of non-NaN range_sample is the same for all pings, AND
# - all pings have the same max range
num_nan = np.isnan(ds_ch.values).sum(axis=1)
nan_check = True if np.all(num_nan == 0) or np.unique(num_nan).size == 1 else False
if not nan_check:
return xr.DataArray(False, coords={"channel": ds_ch["channel"]})
else:
# max range of each ping should be identical
max_range_ping = ds_ch.values[np.arange(ds_ch.shape[0]), ds_ch.shape[1] - num_nan - 1]
if np.unique(max_range_ping).size == 1:
return xr.DataArray(True, coords={"channel": ds_ch["channel"]})
else:
return xr.DataArray(False, coords={"channel": ds_ch["channel"]})
def get_depth_bin_info(ds_Sv, cell_depth):
"""
Find binning indices along depth
"""
depth_ping1 = ds_Sv["depth"].isel(ping_time=0)
num_nan = np.isnan(depth_ping1.values).sum(axis=1)
# ping 1 max range of each channel
max_range_ch = depth_ping1.values[
np.arange(depth_ping1.shape[0]), depth_ping1.shape[1] - num_nan - 1
]
bin_num_depth = np.ceil(max_range_ch.max() / cell_depth) # use max range of all channel
depth_bin_idx = [
np.digitize(dp1, np.arange(bin_num_depth + 1) * cell_depth, right=False)
for dp1 in depth_ping1
]
return bin_num_depth, depth_bin_idx
def get_distance_from_latlon(ds_Sv):
# Get distance from lat/lon in nautical miles
df_pos = ds_Sv["latitude"].to_dataframe().join(ds_Sv["longitude"].to_dataframe())
df_pos["latitude_prev"] = df_pos["latitude"].shift(-1)
df_pos["longitude_prev"] = df_pos["longitude"].shift(-1)
df_latlon_nonan = df_pos.dropna().copy()
df_latlon_nonan["dist"] = df_latlon_nonan.apply(
lambda x: distance.distance(
(x["latitude"], x["longitude"]),
(x["latitude_prev"], x["longitude_prev"]),
).nm,
axis=1,
)
df_pos = df_pos.join(df_latlon_nonan["dist"], how="left")
df_pos["dist"] = df_pos["dist"].cumsum()
df_pos["dist"] = df_pos["dist"].fillna(method="ffill").fillna(method="bfill")
return df_pos["dist"].values
def get_dist_bin_info(dist_nmi, cell_dist):
bin_num_dist = np.ceil(dist_nmi.max() / cell_dist)
if np.mod(dist_nmi.max(), cell_dist) == 0:
# increment bin num if last element coincides with bin edge
bin_num_dist = bin_num_dist + 1
dist_bin_idx = np.digitize(dist_nmi, np.arange(bin_num_dist + 1) * cell_dist, right=False)
return bin_num_dist, dist_bin_idx
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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,824 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/echodata.py | import datetime
import shutil
import warnings
from html import escape
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Tuple, Union
import fsspec
import numpy as np
import xarray as xr
from datatree import DataTree, open_datatree
from zarr.errors import GroupNotFoundError, PathNotFoundError
if TYPE_CHECKING:
from ..core import EngineHint, FileFormatHint, PathHint, SonarModelsHint
from ..utils.coding import sanitize_dtypes, set_time_encodings
from ..utils.io import check_file_existence, sanitize_file_path
from ..utils.log import _init_logger
from ..utils.prov import add_processing_level
from .convention import sonarnetcdf_1
from .sensor_ep_version_mapping import ep_version_mapper
from .widgets.utils import tree_repr
from .widgets.widgets import _load_static_files, get_template
XARRAY_ENGINE_MAP: Dict["FileFormatHint", "EngineHint"] = {
".nc": "netcdf4",
".zarr": "zarr",
}
TVG_CORRECTION_FACTOR = {
"EK60": 2,
"ES70": 2,
"EK80": 0,
"ES80": 0,
"EA640": 0,
}
logger = _init_logger(__name__)
class EchoData:
"""Echo data model class for handling raw converted data,
including multiple files associated with the same data set.
"""
group_map: Dict[str, Any] = sonarnetcdf_1.yaml_dict["groups"]
def __init__(
self,
converted_raw_path: Optional["PathHint"] = None,
storage_options: Optional[Dict[str, Any]] = None,
source_file: Optional["PathHint"] = None,
xml_path: Optional["PathHint"] = None,
sonar_model: Optional["SonarModelsHint"] = None,
open_kwargs: Optional[Dict[str, Any]] = None,
parsed2zarr_obj=None,
):
# TODO: consider if should open datasets in init
# or within each function call when echodata is used. Need to benchmark.
self.storage_options: Dict[str, Any] = (
storage_options if storage_options is not None else {}
)
self.open_kwargs: Dict[str, Any] = open_kwargs if open_kwargs is not None else {}
self.source_file: Optional["PathHint"] = source_file
self.xml_path: Optional["PathHint"] = xml_path
self.sonar_model: Optional["SonarModelsHint"] = sonar_model
self.converted_raw_path: Optional["PathHint"] = converted_raw_path
self._tree: Optional["DataTree"] = None
# object associated with directly writing to a zarr file
self.parsed2zarr_obj = parsed2zarr_obj
self.__setup_groups()
# self.__read_converted(converted_raw_path)
self._varattrs = sonarnetcdf_1.yaml_dict["variable_and_varattributes"]
def __del__(self):
# TODO: this destructor seems to not work in Jupyter Lab if restart or
# even clear all outputs is used. It will work if you explicitly delete the object
if (self.parsed2zarr_obj is not None) and (self.parsed2zarr_obj.zarr_file_name is not None):
# get Path object of temporary zarr file created by Parsed2Zarr
p2z_temp_file = Path(self.parsed2zarr_obj.zarr_file_name)
# remove temporary directory created by Parsed2Zarr, if it exists
if p2z_temp_file.exists():
# TODO: do we need to check file permissions here?
shutil.rmtree(p2z_temp_file)
def __str__(self) -> str:
fpath = "Internal Memory"
if self.converted_raw_path:
fpath = self.converted_raw_path
repr_str = "No data found."
if self._tree is not None:
repr_str = tree_repr(self._tree)
return f"<EchoData: standardized raw data from {fpath}>\n{repr_str}"
def __repr__(self) -> str:
return str(self)
def _repr_html_(self) -> str:
"""Make html representation of InferenceData object."""
_, css_style = _load_static_files()
try:
from xarray.core.options import OPTIONS
display_style = OPTIONS["display_style"]
if display_style == "text":
html_repr = f"<pre>{escape(repr(self))}</pre>"
else:
return get_template("echodata.html.j2").render(echodata=self, css_style=css_style)
except: # noqa
html_repr = f"<pre>{escape(repr(self))}</pre>"
return html_repr
def __setup_groups(self):
for group in self.group_map.keys():
setattr(self, group, None)
def __read_converted(self, converted_raw_path: Optional["PathHint"]):
if converted_raw_path is not None:
self._check_path(converted_raw_path)
converted_raw_path = self._sanitize_path(converted_raw_path)
self._load_file(converted_raw_path)
if isinstance(converted_raw_path, fsspec.FSMap):
# Convert fsmap to Path so it can be used
# for retrieving the path strings
converted_raw_path = Path(converted_raw_path.root)
self.converted_raw_path = converted_raw_path
def _set_tree(self, tree: DataTree):
self._tree = tree
@classmethod
def from_file(
cls,
converted_raw_path: str,
storage_options: Optional[Dict[str, Any]] = None,
open_kwargs: Dict[str, Any] = {},
) -> "EchoData":
echodata = cls(
converted_raw_path=converted_raw_path,
storage_options=storage_options,
open_kwargs=open_kwargs,
)
echodata._check_path(converted_raw_path)
converted_raw_path = echodata._sanitize_path(converted_raw_path)
suffix = echodata._check_suffix(converted_raw_path)
tree = open_datatree(
converted_raw_path,
engine=XARRAY_ENGINE_MAP[suffix],
**echodata.open_kwargs,
)
tree.name = "root"
echodata._set_tree(tree)
# convert to newest echopype version structure, if necessary
ep_version_mapper.map_ep_version(echodata)
if isinstance(converted_raw_path, fsspec.FSMap):
# Convert fsmap to Path so it can be used
# for retrieving the path strings
converted_raw_path = Path(converted_raw_path.root)
echodata.converted_raw_path = converted_raw_path
echodata._load_tree()
return echodata
def _load_tree(self) -> None:
if self._tree is None:
raise ValueError("Datatree not found!")
for group, value in self.group_map.items():
# EK80 data may have a Beam_power group if both complex and power data exist.
ds = None
try:
if value["ep_group"] is None:
node = self._tree
else:
node = self._tree[value["ep_group"]]
ds = self.__get_dataset(node)
except KeyError:
# Skips group not found errors for EK80 and ADCP
...
if group == "top" and hasattr(ds, "keywords"):
self.sonar_model = ds.keywords.upper() # type: ignore
if isinstance(ds, xr.Dataset):
setattr(self, group, node)
@property
def version_info(self) -> Union[Tuple[int], None]:
def _get_version_tuple(provenance_type):
"""
Parameters
----------
provenance_type : str
Either conversion or combination
"""
version_str = self["Provenance"].attrs.get(f"{provenance_type}_software_version", None)
if version_str is not None:
if version_str.startswith("v"):
# Removes v in case of v0.4.x or less
version_str = version_str.strip("v")
version_num = version_str.split(".")[:3]
return tuple([int(i) for i in version_num])
if self["Provenance"].attrs.get("combination_software_name", None) == "echopype":
return _get_version_tuple("combination")
elif self["Provenance"].attrs.get("conversion_software_name", None) == "echopype":
return _get_version_tuple("conversion")
else:
return None
@property
def nbytes(self) -> float:
return float(sum(self[p].nbytes for p in self.group_paths))
@property
def group_paths(self) -> Set[str]:
return tuple(i[1:] if i != "/" else "Top-level" for i in self._tree.groups)
@staticmethod
def __get_dataset(node: DataTree) -> Optional[xr.Dataset]:
if node.has_data or node.has_attrs:
# validate and clean dtypes
return sanitize_dtypes(node.ds)
return None
def __get_node(self, key: Optional[str]) -> DataTree:
if key in ["Top-level", "/"]:
# Access to root
return self._tree
return self._tree[key]
def __getitem__(self, __key: Optional[str]) -> Optional[xr.Dataset]:
if self._tree:
try:
node = self.__get_node(__key)
return self.__get_dataset(node)
except KeyError:
return None
else:
raise ValueError("Datatree not found!")
def __setitem__(self, __key: Optional[str], __newvalue: Any) -> Optional[xr.Dataset]:
if self._tree:
try:
node = self.__get_node(__key)
node.ds = __newvalue
return self.__get_dataset(node)
except KeyError:
raise GroupNotFoundError(__key)
else:
raise ValueError("Datatree not found!")
def __setattr__(self, __name: str, __value: Any) -> None:
attr_value = __value
if isinstance(__value, DataTree) and __name != "_tree":
attr_value = self.__get_dataset(__value)
elif isinstance(__value, xr.Dataset):
group_map = sonarnetcdf_1.yaml_dict["groups"]
if __name in group_map:
group = group_map.get(__name)
group_path = group["ep_group"]
if self._tree:
if __name == "top":
self._tree.ds = __value
else:
self._tree[group_path].ds = __value
super().__setattr__(__name, attr_value)
@add_processing_level("L1A")
def update_platform(
self,
extra_platform_data: xr.Dataset,
variable_mappings=Dict[str, str],
extra_platform_data_file_name=None,
):
"""
Updates the `EchoData["Platform"]` group with additional external platform data.
`extra_platform_data` must be an xarray Dataset. Data is extracted from
`extra_platform_data` by variable name. Only data assigned to a pre-existing
(but possibly all-nan) variable name in Platform will be processed. These
Platform variables include latitude, longitude, pitch, roll, vertical_offset, etc.
See the variables present in the EchoData object's Platform group to obtain a
complete list of possible variables.
Different external variables may be dependent on different time dimensions, but
latitude and longitude (if specified) must share the same time dimension. New
time dimensions will be added as needed. For example, if variables to be added
from the external data use two time dimensions and the Platform group has time
dimensions time2 and time2, new dimensions time3 and time4 will be created.
Parameters
----------
extra_platform_data : xr.Dataset
An `xr.Dataset` containing the additional platform data to be added
to the `EchoData["Platform"]` group.
variable_mappings: Dict[str,str]
A dictionary mapping Platform variable names (dict key) to the
external-data variable name (dict value).
extra_platform_data_file_name: str, default=None
File name for source of extra platform data, if read from a file
Examples
--------
>>> ed = echopype.open_raw(raw_file, "EK60")
>>> extra_platform_data = xr.open_dataset(extra_platform_data_file_name)
>>> ed.update_platform(
>>> extra_platform_data,
>>> variable_mappings={"longitude": "lon", "latitude": "lat", "roll": "ROLL"},
>>> extra_platform_data_file_name=extra_platform_data_file_name
>>> )
"""
# Handle data stored as a CF Trajectory Discrete Sampling Geometry
# http://cfconventions.org/Data/cf-conventions/cf-conventions-1.8/cf-conventions.html#trajectory-data
# The Saildrone sample data file follows this convention
if (
"featureType" in extra_platform_data.attrs
and extra_platform_data.attrs["featureType"].lower() == "trajectory"
):
for coordvar in extra_platform_data.coords:
coordvar_attrs = extra_platform_data[coordvar].attrs
if "cf_role" in coordvar_attrs and coordvar_attrs["cf_role"] == "trajectory_id":
trajectory_var = coordvar
if "standard_name" in coordvar_attrs and coordvar_attrs["standard_name"] == "time":
time_dim = coordvar
# assumes there's only one trajectory in the dataset (index 0)
extra_platform_data = extra_platform_data.sel(
{trajectory_var: extra_platform_data[trajectory_var][0]}
)
extra_platform_data = extra_platform_data.drop_vars(trajectory_var)
obs_dim = list(extra_platform_data[time_dim].dims)[0]
extra_platform_data = extra_platform_data.swap_dims({obs_dim: time_dim})
def _extvar_properties(ds, name):
"""Test the external variable for presence and all-nan values,
and extract its time dimension name.
Returns <presence>, <valid values>, <time dim name>
"""
if name in ds:
# Assumes the only dimension in the variable is a time dimension
time_dim_name = ds[name].dims[0] if len(ds[name].dims) > 0 else "scalar"
if not ds[name].isnull().all():
return True, True, time_dim_name
else:
return True, False, time_dim_name
else:
return False, False, None
# clip incoming time to 1 less than min of EchoData["Sonar/Beam_group1"]["ping_time"] and
# 1 greater than max of EchoData["Sonar/Beam_group1"]["ping_time"]
# account for unsorted external time by checking whether each time value is between
# min and max ping_time instead of finding the 2 external times corresponding to the
# min and max ping_time and taking all the times between those indices
def _clip_by_time_dim(external_ds, ext_time_dim_name):
# external_ds is extra_platform_data[vars-list-on-time_dim_name]
sorted_external_time = external_ds[ext_time_dim_name].data
sorted_external_time.sort()
# fmt: off
min_index = max(
np.searchsorted(
sorted_external_time,
self["Sonar/Beam_group1"]["ping_time"].min(),
side="left"
) - 1,
0,
)
# fmt: on
max_index = min(
np.searchsorted(
sorted_external_time,
self["Sonar/Beam_group1"]["ping_time"].max(),
side="right",
),
len(sorted_external_time) - 1,
)
# TODO: this element-wise comparison is expensive and seems an ad-hoc patch
# to deal with potentially reversed timestamps in the external dataset.
# Review at workflow stage to see if to clean up timestamp reversals
# and just find start/end timestamp for slicing.
return external_ds.sel(
{
ext_time_dim_name: np.logical_and(
sorted_external_time[min_index] <= external_ds[ext_time_dim_name],
external_ds[ext_time_dim_name] <= sorted_external_time[max_index],
)
}
)
# History attribute to be included in each updated variable
history_attr = f"{datetime.datetime.utcnow()} +00:00. Added from external platform data"
if extra_platform_data_file_name:
history_attr += ", from file " + extra_platform_data_file_name
platform = self["Platform"]
# Retain only variable_mappings items where
# either the Platform group or extra_platform_data
# contain the corresponding variables or contain valid (not all nan) data
mappings_expanded = {}
for platform_var, external_var in variable_mappings.items():
# TODO: instead of using existing Platform group variables, a better practice is to
# define a set of allowable Platform variables (sonar_model dependent) for this check.
# This set can be dynamically generated from an external source like a CDL or yaml.
if platform_var in platform:
platform_validvalues = not platform[platform_var].isnull().all()
ext_present, ext_validvalues, ext_time_dim_name = _extvar_properties(
extra_platform_data, external_var
)
if ext_present and ext_validvalues:
mappings_expanded[platform_var] = dict(
external_var=external_var,
ext_time_dim_name=ext_time_dim_name,
platform_validvalues=platform_validvalues,
)
# Generate warning if mappings_expanded is empty
if not mappings_expanded:
logger.warning(
"No variables will be updated, "
"check variable_mappings to ensure variable names are correctly specified!"
)
# If longitude or latitude are requested, verify that both are present
# and they share the same external time dimension
if "longitude" in mappings_expanded or "latitude" in mappings_expanded:
if "longitude" not in mappings_expanded or "latitude" not in mappings_expanded:
raise ValueError(
"Only one of latitude and longitude are specified. Please include both, or neither." # noqa
)
if (
mappings_expanded["longitude"]["ext_time_dim_name"]
!= mappings_expanded["latitude"]["ext_time_dim_name"]
):
raise ValueError(
"The external latitude and longitude use different time dimensions. "
"They must share the same time dimension."
)
# Generate warnings regarding variables that will be updated
vars_not_handled = set(variable_mappings.keys()).difference(mappings_expanded.keys())
if len(vars_not_handled) > 0:
logger.warning(
f"The following requested variables will not be updated: {', '.join(vars_not_handled)}" # noqa
)
vars_notnan_replaced = [
platform_var
for platform_var, v in mappings_expanded.items()
if v["platform_validvalues"]
]
if len(vars_notnan_replaced) > 0:
logger.warning(
f"Some variables with valid data in the original Platform group will be overwritten: {', '.join(vars_notnan_replaced)}" # noqa
)
# Create names for required new time dimensions
ext_time_dims = list(
{
v["ext_time_dim_name"]
for v in mappings_expanded.values()
if v["ext_time_dim_name"] != "scalar"
}
)
time_dims_max = max([int(dim[-1]) for dim in platform.dims if dim.startswith("time")])
new_time_dims = [f"time{time_dims_max+i+1}" for i in range(len(ext_time_dims))]
# Map each new time dim name to the external time dim name:
new_time_dims_mappings = {new: ext for new, ext in zip(new_time_dims, ext_time_dims)}
# Process variable updates by corresponding new time dimensions
for time_dim in new_time_dims:
ext_time_dim = new_time_dims_mappings[time_dim]
mappings_selected = {
k: v for k, v in mappings_expanded.items() if v["ext_time_dim_name"] == ext_time_dim
}
ext_vars = [v["external_var"] for v in mappings_selected.values()]
ext_ds = _clip_by_time_dim(extra_platform_data[ext_vars], ext_time_dim)
# Create new time coordinate and dimension
platform = platform.assign_coords(**{time_dim: ext_ds[ext_time_dim].values})
time_attrs = {
"axis": "T",
"standard_name": "time",
"long_name": "Timestamps from an external dataset",
"comment": "Time coordinate originated from a dataset "
"external to the sonar data files.",
"history": f"{history_attr}. From external {ext_time_dim} variable.",
}
platform[time_dim] = platform[time_dim].assign_attrs(**time_attrs)
# Process each platform variable that will be replaced
for platform_var in mappings_selected.keys():
ext_var = mappings_expanded[platform_var]["external_var"]
platform_var_attrs = platform[platform_var].attrs.copy()
# Create new (replaced) variable using dataset "update"
# With update, dropping the variable first is not needed
platform = platform.update({platform_var: (time_dim, ext_ds[ext_var].data)})
# Assign attributes to newly created (replaced) variables
var_attrs = platform_var_attrs
var_attrs["history"] = f"{history_attr}. From external {ext_var} variable."
platform[platform_var] = platform[platform_var].assign_attrs(**var_attrs)
# Update scalar variables, if any
scalar_vars = [
platform_var
for platform_var, v in mappings_expanded.items()
if v["ext_time_dim_name"] == "scalar"
]
for platform_var in scalar_vars:
ext_var = mappings_expanded[platform_var]["external_var"]
# Replace the scalar value and add a history attribute
platform[platform_var].data = float(extra_platform_data[ext_var].data)
platform[platform_var] = platform[platform_var].assign_attrs(
**{"history": f"{history_attr}. From external {ext_var} variable."}
)
# Drop pre-existing time dimensions that are no longer being used
used_dims = {
platform[platform_var].dims[0]
for platform_var in platform.data_vars
if len(platform[platform_var].dims) > 0
}
platform = platform.drop_dims(set(platform.dims).difference(used_dims), errors="ignore")
self["Platform"] = set_time_encodings(platform)
@classmethod
def _load_convert(cls, convert_obj):
new_cls = cls()
for group in new_cls.group_map.keys():
if hasattr(convert_obj, group):
setattr(new_cls, group, getattr(convert_obj, group))
setattr(new_cls, "sonar_model", getattr(convert_obj, "sonar_model"))
setattr(new_cls, "source_file", getattr(convert_obj, "source_file"))
return new_cls
def _load_file(self, raw_path: "PathHint"):
"""Lazy load all groups and subgroups from raw file."""
for group, value in self.group_map.items():
# EK80 data may have a Beam_power group if both complex and power data exist.
ds = None
try:
ds = self._load_group(
raw_path,
group=value["ep_group"],
)
except (OSError, GroupNotFoundError, PathNotFoundError):
# Skips group not found errors for EK80 and ADCP
...
if group == "top" and hasattr(ds, "keywords"):
self.sonar_model = ds.keywords.upper() # type: ignore
if isinstance(ds, xr.Dataset):
setattr(self, group, ds)
def _check_path(self, filepath: "PathHint"):
"""Check if converted_raw_path exists"""
file_exists = check_file_existence(filepath, self.storage_options)
if not file_exists:
raise FileNotFoundError(f"There is no file named {filepath}")
def _sanitize_path(self, filepath: "PathHint") -> "PathHint":
filepath = sanitize_file_path(filepath, self.storage_options)
return filepath
def _check_suffix(self, filepath: "PathHint") -> "FileFormatHint":
"""Check if file type is supported."""
# TODO: handle multiple files through the same set of checks for combining files
if isinstance(filepath, fsspec.FSMap):
suffix = Path(filepath.root).suffix
else:
suffix = Path(filepath).suffix
if suffix not in XARRAY_ENGINE_MAP:
raise ValueError("Input file type not supported!")
return suffix # type: ignore
def _load_group(self, filepath: "PathHint", group: Optional[str] = None):
"""Loads each echodata group"""
suffix = self._check_suffix(filepath)
return xr.open_dataset(
filepath,
group=group,
engine=XARRAY_ENGINE_MAP[suffix],
**self.open_kwargs,
)
def to_netcdf(
self,
save_path: Optional["PathHint"] = None,
compress: bool = True,
overwrite: bool = False,
parallel: bool = False,
output_storage_options: Dict[str, str] = {},
**kwargs,
):
"""Save content of EchoData to netCDF.
Parameters
----------
save_path : str
path that converted .nc file will be saved
compress : bool
whether or not to perform compression on data variables
Defaults to ``True``
overwrite : bool
whether or not to overwrite existing files
Defaults to ``False``
parallel : bool
whether or not to use parallel processing. (Not yet implemented)
output_storage_options : dict
Additional keywords to pass to the filesystem class.
**kwargs : dict, optional
Extra arguments to `xr.Dataset.to_netcdf`: refer to
xarray's documentation for a list of all possible arguments.
"""
from ..convert.api import to_file
return to_file(
echodata=self,
engine="netcdf4",
save_path=save_path,
compress=compress,
overwrite=overwrite,
parallel=parallel,
output_storage_options=output_storage_options,
**kwargs,
)
def to_zarr(
self,
save_path: Optional["PathHint"] = None,
compress: bool = True,
overwrite: bool = False,
parallel: bool = False,
output_storage_options: Dict[str, str] = {},
consolidated: bool = True,
**kwargs,
):
"""Save content of EchoData to zarr.
Parameters
----------
save_path : str
path that converted .nc file will be saved
compress : bool
whether or not to perform compression on data variables
Defaults to ``True``
overwrite : bool
whether or not to overwrite existing files
Defaults to ``False``
parallel : bool
whether or not to use parallel processing. (Not yet implemented)
output_storage_options : dict
Additional keywords to pass to the filesystem class.
consolidated : bool
Flag to consolidate zarr metadata.
Defaults to ``True``
**kwargs : dict, optional
Extra arguments to `xr.Dataset.to_zarr`: refer to
xarray's documentation for a list of all possible arguments.
"""
from ..convert.api import to_file
return to_file(
echodata=self,
engine="zarr",
save_path=save_path,
compress=compress,
overwrite=overwrite,
parallel=parallel,
output_storage_options=output_storage_options,
consolidated=consolidated,
**kwargs,
)
# TODO: Remove below in future versions. They are for supporting old API calls.
@property
def nc_path(self) -> Optional["PathHint"]:
warnings.warn(
"`nc_path` is deprecated, Use `converted_raw_path` instead.",
DeprecationWarning,
2,
)
if self.converted_raw_path.endswith(".nc"):
return self.converted_raw_path
else:
path = Path(self.converted_raw_path)
return str(path.parent / (path.stem + ".nc"))
@property
def zarr_path(self) -> Optional["PathHint"]:
warnings.warn(
"`zarr_path` is deprecated, Use `converted_raw_path` instead.",
DeprecationWarning,
2,
)
if self.converted_raw_path.endswith(".zarr"):
return self.converted_raw_path
else:
path = Path(self.converted_raw_path)
return str(path.parent / (path.stem + ".zarr"))
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,825 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/visualize/api.py | from typing import Optional, Union, List, Type
import xarray as xr
from .plot import _plot_echogram, FacetGrid, QuadMesh
from ..echodata import EchoData
from ..calibrate.calibrate_ek import CalibrateEK60, CalibrateEK80
from ..calibrate.calibrate_azfp import CalibrateAZFP
from ..utils.log import _init_logger
logger = _init_logger(__name__)
def create_echogram(
data: Union[EchoData, xr.Dataset],
channel: Union[str, List[str], None] = None,
frequency: Union[str, List[str], None] = None,
get_range: Optional[bool] = None,
range_kwargs: dict = {},
vertical_offset: Union[int, float, xr.DataArray, bool, None] = None,
**kwargs,
) -> List[Union[FacetGrid, QuadMesh]]:
"""Create an Echogram from an EchoData object or Sv and MVBS Dataset.
Parameters
----------
data : EchoData or xr.Dataset
Echodata or Xarray Dataset to be plotted
channel : str or list of str, optional
The channel to be plotted.
Otherwise all channels will be plotted.
frequency : int, float, or list of float or ints, optional
The frequency to be plotted.
If not specified, all frequency will be plotted.
get_range : bool, optional
Flag as to whether range (``echo_range``) should be computed or not,
by default it will just plot `range_sample`` as the yaxis.
Note that for data that is "Sv" xarray dataset, `get_range` defaults
to `True`.
range_kwargs : dict
Keyword arguments dictionary for computing range (``echo_range``).
Keys are `env_params`, `waveform_mode`, and `encode_mode`.
vertical_offset : int, float, xr.DataArray, or bool, optional
Water level data array for platform water level correction.
Note that auto addition of water level can be performed
when data is an EchoData object by setting this argument
to `True`. Currently because the water level information
is not available as part of the Sv dataset, a warning is issued
when `vertical_offset=True` in this case and no correction is
performed. This behavior will change in the future when the
default content of Sv dataset is updated to include this information.
**kwargs: optional
Additional keyword arguments for xarray plot pcolormesh.
Notes
-----
The EK80 echosounder can be configured to transmit
either broadband (``waveform_mode="BB"``)
or narrowband (``waveform_mode="CW"``) signals.
When transmitting in broadband mode, the returned echoes are
encoded as complex samples (``encode_mode="complex"``).
When transmitting in narrowband mode, the returned echoes can be encoded
either as complex samples (``encode_mode="complex"``)
or as power/angle combinations (``encode_mode="power"``) in a format
similar to those recorded by EK60 echosounders.
"""
range_attrs = {
'long_name': 'Range',
'units': 'm',
}
if channel and frequency:
logger.warning(
"Both channel and frequency are specified. Channel filtering will be used."
)
if isinstance(channel, list) and len(channel) == 1:
channel = channel[0]
elif isinstance(frequency, list) and len(frequency) == 1:
frequency = frequency[0]
if isinstance(data, EchoData):
if data.sonar_model.lower() == 'ad2cp':
raise ValueError(
"Visualization for AD2CP sonar model is currently unsupported."
)
yaxis = 'range_sample'
variable = 'backscatter_r'
ds = data["Sonar/Beam_group1"]
if 'ping_time' in ds:
_check_ping_time(ds.ping_time)
if get_range is True:
yaxis = 'echo_range'
if data.sonar_model.lower() == 'azfp':
if 'azfp_cal_type' not in range_kwargs:
range_kwargs['azfp_cal_type'] = 'Sv'
if 'env_params' not in range_kwargs:
raise ValueError(
"Please provide env_params in range_kwargs!"
)
elif data.sonar_model.lower() == 'ek60':
if 'waveform_mode' not in range_kwargs:
range_kwargs['waveform_mode'] = 'CW'
elif range_kwargs['waveform_mode'] != 'CW':
raise ValueError(
f"waveform_mode {range_kwargs['waveform_mode']} is invalid. EK60 waveform_mode must be 'CW'." # noqa
)
if 'encode_mode' not in range_kwargs:
range_kwargs['encode_mode'] = 'power'
elif range_kwargs['encode_mode'] != 'power':
raise ValueError(
f"encode_mode {range_kwargs['encode_mode']} is invalid. EK60 encode_mode must be 'power'." # noqa
)
elif data.sonar_model.lower() == 'ek80':
if not all(
True if mode in range_kwargs else False
for mode in ['waveform_mode', 'encode_mode']
):
raise ValueError(
"Please provide waveform_mode and encode_mode in range_kwargs for EK80 sonar model." # noqa
)
waveform_mode = range_kwargs['waveform_mode']
encode_mode = range_kwargs['encode_mode']
if waveform_mode not in ("BB", "CW"):
raise ValueError(
f"waveform_mode {waveform_mode} is invalid. EK80 waveform_mode must be 'BB' or 'CW'." # noqa
)
elif encode_mode not in ("complex", "power"):
raise ValueError(
f"encode_mode {encode_mode} is invalid. EK80 waveform_mode must be 'complex' or 'power'." # noqa
)
elif waveform_mode == "BB" and encode_mode == "power":
raise ValueError(
"Data from broadband ('BB') transmission must be recorded as complex samples" # noqa
)
# Compute range via calibration objects
if data.sonar_model == "AZFP":
cal_obj = CalibrateAZFP(
echodata=data,
env_params=range_kwargs.get("env_params", {}),
cal_params=None,
waveform_mode=None,
encode_mode=None,
)
if range_kwargs["azfp_cal_type"] is None:
raise ValueError("azfp_cal_type must be specified when sonar_model is AZFP")
cal_obj.compute_echo_range(cal_type=range_kwargs["azfp_cal_type"])
elif data.sonar_model in ("EK60", "EK80", "ES70", "ES80", "EA640"):
if data.sonar_model in ["EK60", "ES70"]:
cal_obj = CalibrateEK60(
echodata=data,
env_params=range_kwargs.get("env_params", {}),
cal_params=None,
ecs_file=None,
)
else:
cal_obj = CalibrateEK80(
echodata=data,
env_params=range_kwargs.get("env_params", {}),
cal_params=None,
ecs_file=None,
waveform_mode=range_kwargs.get("waveform_mode", "CW"),
encode_mode=range_kwargs.get("encode_mode", "power"),
)
range_in_meter = cal_obj.range_meter
range_in_meter.attrs = range_attrs
if vertical_offset is not None:
range_in_meter = _add_vertical_offset(
range_in_meter=range_in_meter,
vertical_offset=vertical_offset,
data_type=EchoData,
platform_data=data["Platform"],
)
ds = ds.assign_coords({'echo_range': range_in_meter})
ds.echo_range.attrs = range_attrs
elif isinstance(data, xr.Dataset):
if 'ping_time' in data:
_check_ping_time(data.ping_time)
variable = 'Sv'
ds = data
yaxis = 'echo_range'
if 'echo_range' not in data.dims and get_range is False:
# Range in dims indicates that data is MVBS.
yaxis = 'range_sample'
# If depth is available in ds, use it.
ds = ds.set_coords('echo_range')
if vertical_offset is not None:
ds['echo_range'] = _add_vertical_offset(
range_in_meter=ds.echo_range,
vertical_offset=vertical_offset,
data_type=xr.Dataset,
)
ds.echo_range.attrs = range_attrs
else:
raise ValueError(f"Unsupported data type: {type(data)}")
return _plot_echogram(
ds,
xaxis='ping_time',
yaxis=yaxis,
variable=variable,
channel=channel,
frequency=frequency,
**kwargs,
)
def _check_ping_time(ping_time):
if ping_time.shape[0] < 2:
raise ValueError("Ping time must have a length that is greater or equal to 2")
def _add_vertical_offset(
range_in_meter: xr.DataArray,
vertical_offset: Union[int, float, xr.DataArray, bool],
data_type: Union[Type[xr.Dataset], Type[EchoData]],
platform_data: Optional[xr.Dataset] = None,
) -> xr.DataArray:
# Below, we rename time2 to ping_time because range_in_meter is in ping_time
if isinstance(vertical_offset, bool):
if vertical_offset is True:
if data_type == xr.Dataset:
logger.warning(
"Boolean type found for vertical_offset. Ignored since data is an xarray dataset."
)
return range_in_meter
elif data_type == EchoData:
if (
isinstance(platform_data, xr.Dataset)
and 'vertical_offset' in platform_data
):
return range_in_meter + platform_data.vertical_offset.rename({'time2': 'ping_time'})
else:
logger.warning(
"Boolean type found for vertical_offset. Please provide platform data with vertical_offset in it or provide a separate vertical_offset data." # noqa
)
return range_in_meter
logger.warning(f"vertical_offset value of {vertical_offset} is ignored.")
return range_in_meter
if isinstance(vertical_offset, xr.DataArray):
check_dims = list(range_in_meter.dims)
check_dims.remove('channel')
if 'time2' in vertical_offset:
vertical_offset = vertical_offset.rename({'time2': 'ping_time'})
if not any(
True if d in vertical_offset.dims else False for d in check_dims
):
raise ValueError(
f"vertical_offset must have any of these dimensions: {', '.join(check_dims)}"
)
# Adds vertical_offset to range if it exists
return range_in_meter + vertical_offset
elif isinstance(vertical_offset, (int, float)):
return range_in_meter + vertical_offset
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,826 | OSOceanAcoustics/echopype | refs/heads/main | /.ci_helpers/docker/setup-services.py | """setup-services.py
Script to help bring up docker services for testing.
"""
import argparse
import logging
import shutil
import subprocess
import sys
from pathlib import Path
from typing import Dict, List
import fsspec
logger = logging.getLogger("setup-services")
streamHandler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(message)s")
streamHandler.setFormatter(formatter)
logger.addHandler(streamHandler)
logger.setLevel(level=logging.INFO)
HERE = Path(".").absolute()
BASE = Path(__file__).parent.absolute()
COMPOSE_FILE = BASE / "docker-compose.yaml"
TEST_DATA_PATH = HERE / "echopype" / "test_data"
def parse_args():
parser = argparse.ArgumentParser(description="Setup services for testing")
parser.add_argument("--deploy", action="store_true", help="Flag to setup docker services")
parser.add_argument(
"--http-server",
default="docker_httpserver_1",
help="Flag for specifying docker http server id.",
)
parser.add_argument(
"--no-pull",
action="store_true",
help="Optional flag to skip pulling the latest images from dockerhub",
)
parser.add_argument(
"--data-only",
action="store_true",
help="""Optional flag to only copy over data to http server,
and setup minio bucket and not deploy any services. NOTE: MUST HAVE SERVICES RUNNING!""",
)
parser.add_argument(
"--tear-down",
action="store_true",
help="Flag to tear down docker services",
)
parser.add_argument(
"--images",
action="store_true",
help="Optional flag to remove images also during tear down",
)
return parser.parse_args()
def run_commands(commands: List[Dict]) -> None:
for idx, command in enumerate(commands, start=1):
msg = command.get("msg")
cmd = command.get("cmd")
args = command.get("args", None)
logger.info(f"{idx}) {msg}")
if cmd is None:
continue
elif isinstance(cmd, list):
subprocess.run(cmd)
elif callable(cmd):
cmd(args)
else:
raise ValueError(f"command of {type(cmd)} is invalid.")
def load_s3(*args, **kwargs) -> None:
common_storage_options = dict(
client_kwargs=dict(endpoint_url="http://localhost:9000/"),
key="minioadmin",
secret="minioadmin",
)
bucket_name = "ooi-raw-data"
fs = fsspec.filesystem(
"s3",
**common_storage_options,
)
test_data = "data"
if not fs.exists(test_data):
fs.mkdir(test_data)
if not fs.exists(bucket_name):
fs.mkdir(bucket_name)
# Load test data into bucket
for d in TEST_DATA_PATH.iterdir():
source_path = f"echopype/test_data/{d.name}"
fs.put(source_path, f"{test_data}/{d.name}", recursive=True)
if __name__ == "__main__":
args = parse_args()
commands = []
if all([args.deploy, args.tear_down]):
print("Cannot have both --deploy and --tear-down. Exiting.")
sys.exit(1)
if not any([args.deploy, args.tear_down]):
print("Please provide either --deploy or --tear-down flags. For more help use --help flag.")
sys.exit(0)
if args.deploy:
commands.append({"msg": "Starting test services deployment ...", "cmd": None})
if not args.data_only:
if not args.no_pull:
commands.append(
{
"msg": "Pulling latest images ...",
"cmd": ["docker-compose", "-f", COMPOSE_FILE, "pull"],
}
)
commands.append(
{
"msg": "Bringing up services ...",
"cmd": [
"docker-compose",
"-f",
COMPOSE_FILE,
"up",
"-d",
"--remove-orphans",
"--force-recreate",
],
}
)
if TEST_DATA_PATH.exists():
commands.append(
{
"msg": f"Deleting old test folder at {TEST_DATA_PATH} ...",
"cmd": shutil.rmtree,
"args": TEST_DATA_PATH,
}
)
commands.append(
{
"msg": "Copying new test folder from http service ...",
"cmd": [
"docker",
"cp",
"-L",
f"{args.http_server}:/usr/local/apache2/htdocs/data",
TEST_DATA_PATH,
],
}
)
commands.append({"msg": "Setting up minio s3 bucket ...", "cmd": load_s3})
if args.tear_down:
command = ["docker-compose", "-f", COMPOSE_FILE, "down", "--remove-orphans", "--volumes"]
if args.images:
command = command + ["--rmi", "all"]
commands.append({"msg": "Stopping test services deployment ...", "cmd": command})
commands.append({"msg": "Done.", "cmd": ["docker", "ps", "--last", "2"]})
run_commands(commands)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,827 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py | import xml.etree.ElementTree as ET
import numpy as np
import xarray as xr
# TODO: turn this into an absolute import!
from ...core import SONAR_MODELS
from ...utils.log import _init_logger
from ..convention import sonarnetcdf_1
_varattrs = sonarnetcdf_1.yaml_dict["variable_and_varattributes"]
logger = _init_logger(__name__)
def _get_sensor(sensor_model):
"""
This function returns the sensor name corresponding to
the ``set_groups_X.py`` that was used to create the
v0.5.x file's EchoData structure.
Parameters
----------
sensor_model : str
Sensor model name provided in ``ed['Top-level'].keywords``
"""
if sensor_model in ["EK60", "ES70"]:
return "EK60"
elif sensor_model in ["EK80", "ES80", "EA640"]:
return "EK80"
else:
return sensor_model
def _range_bin_to_range_sample(ed_obj):
"""
Renames the coordinate range_bin to range_sample.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x
Notes
-----
The function directly modifies the input EchoData object.
"""
for grp_path in ed_obj.group_paths:
if "range_bin" in list(ed_obj[grp_path].coords):
# renames range_bin in the dataset
ed_obj[grp_path] = ed_obj[grp_path].rename(name_dict={"range_bin": "range_sample"})
ed_obj[grp_path].range_sample.attrs["long_name"] = "Along-range sample number, base 0"
def _add_attrs_to_freq(ed_obj):
"""
Makes the attributes of the ``frequency`` variable
consistent for all groups. This is necessary because
not all groups have the same attributes (some are
missing them too) for the ``frequency`` variable.
This variable is used to set the variable
``frequency_nominal`` later on.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x
Notes
-----
The function directly modifies the input EchoData object.
"""
for grp_path in ed_obj.group_paths:
if "frequency" in list(ed_obj[grp_path].coords):
# creates consistent frequency attributes
ed_obj[grp_path]["frequency"] = ed_obj[grp_path].frequency.assign_attrs(
{
"long_name": "Transducer frequency",
"standard_name": "sound_frequency",
"units": "Hz",
"valid_min": 0.0,
}
)
def _reorganize_beam_groups(ed_obj):
"""
Maps Beam --> Sonar/Beam_group1 and Beam_power --> Sonar/Beam_group2.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x
Notes
-----
The function directly modifies the input EchoData object.
"""
# Map Beam --> Sonar/Beam_group1
if "Beam" in ed_obj.group_paths:
ed_obj._tree["Sonar/Beam_group1"] = ed_obj._tree["Beam"]
# Map Beam_power --> Sonar/Beam_group2
if "Beam_power" in ed_obj.group_paths:
ed_obj._tree["Sonar/Beam_group2"] = ed_obj._tree["Beam_power"]
def get_channel_id(ed_obj, sensor):
"""
Returns the channel_id for all non-unique frequencies.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
The sensor used to create the v0.5.x file.
Returns
-------
A datarray specifying the channel_ids with dimension frequency.
"""
if sensor == "AZFP":
# create frequency_nominal variable
freq_nom = ed_obj["Sonar/Beam_group1"].frequency
# create unique channel_id for AZFP
freq_as_str = (freq_nom / 1000.0).astype(int).astype(str).values
channel_id_str = [
str(ed_obj["Sonar"].sonar_serial_number) + "-" + freq_as_str[i] + "-" + str(i + 1)
for i in range(len(freq_as_str))
]
channel_id = xr.DataArray(
data=channel_id_str, dims=["frequency"], coords={"frequency": freq_nom}
)
else:
# in the case of EK80 we cannot infer the correct frequency
# for each channel id, but we can obtain this information
# from the XML string in the Vendor attribute
if "config_xml" in ed_obj._tree["Vendor"].ds.attrs:
xmlstring = ed_obj._tree["Vendor"].ds.attrs["config_xml"]
root = ET.fromstring(xmlstring)
channel_ids = []
freq = []
for i in root.findall("./Transceivers/Transceiver"):
[channel_ids.append(j.attrib["ChannelID"]) for j in i.findall(".//Channel")]
[freq.append(np.float64(j.attrib["Frequency"])) for j in i.findall(".//Transducer")]
channel_id = xr.DataArray(data=channel_ids, coords={"frequency": (["frequency"], freq)})
else:
# collect all beam group channel ids and associated frequencies
channel_id = xr.concat(
[child.ds.channel_id for child in ed_obj._tree["Sonar"].children.values()],
dim="frequency",
)
return channel_id
def _frequency_to_channel(ed_obj, sensor):
"""
1. In all groups that it appears, changes the dimension
``frequency`` to ``channel`` whose values are based on
``channel_id`` for EK60/EK80 and are a custom string
for AZFP.
2. Removes channel_id if it appears as a variable
3. Adds the variable ``frequency_nominal`` to all
Datasets that have dimension ``channel``
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
The sensor used to create the v0.5.x file.
Notes
-----
The function directly modifies the input EchoData object.
"""
channel_id = get_channel_id(ed_obj, sensor) # all channel ids
for grp_path in ed_obj.group_paths:
if "frequency" in ed_obj[grp_path]:
# add frequency_nominal
ed_obj[grp_path]["frequency_nominal"] = ed_obj[grp_path].frequency
ed_obj[grp_path] = ed_obj[grp_path].rename({"frequency": "channel"})
# set values for channel
if "channel_id" in ed_obj[grp_path]:
ed_obj[grp_path]["channel"] = ed_obj[grp_path].channel_id.values
ed_obj[grp_path] = ed_obj._tree[grp_path].ds.drop("channel_id")
else:
ed_obj[grp_path]["channel"] = channel_id.sel(
frequency=ed_obj[grp_path].frequency_nominal
).values
# set attributes for channel
ed_obj[grp_path]["channel"] = ed_obj[grp_path]["channel"].assign_attrs(
_varattrs["beam_coord_default"]["channel"]
)
def _change_beam_var_names(ed_obj, sensor):
"""
For EK60 ``Beam_group1``
1. Rename ``beamwidth_receive_alongship`` to
``beamwidth_twoway_alongship`` and change the attribute
``long_name``
2. Rename ``beamwidth_transmit_athwartship`` to
``beamwidth_twoway_athwartship`` and change the attribute
``long_name``
3. Remove the variables ``beamwidth_receive_athwartship``
and ``beamwidth_transmit_alongship``
4. Change the attribute ``long_name`` in the variables
``angle_offset_alongship/athwartship`` and
``angle_sensitivity_alongship/athwartship``
For EK80 ``Beam_group1``
1. Change the attribute ``long_name`` in the variables
``angle_offset_alongship/athwartship``
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor == "EK60":
ed_obj["Sonar/Beam_group1"] = ed_obj["Sonar/Beam_group1"].rename(
{"beamwidth_receive_alongship": "beamwidth_twoway_alongship"}
)
ed_obj["Sonar/Beam_group1"].beamwidth_twoway_alongship.attrs[
"long_name"
] = "Half power two-way beam width along alongship axis of beam"
ed_obj["Sonar/Beam_group1"] = ed_obj["Sonar/Beam_group1"].rename(
{"beamwidth_transmit_athwartship": "beamwidth_twoway_athwartship"}
)
ed_obj["Sonar/Beam_group1"].beamwidth_twoway_athwartship.attrs[
"long_name"
] = "Half power two-way beam width along athwartship axis of beam"
ed_obj["Sonar/Beam_group1"] = ed_obj["Sonar/Beam_group1"].drop(
["beamwidth_receive_athwartship", "beamwidth_transmit_alongship"]
)
if sensor in ["EK60", "EK80"]:
for beam_group in ed_obj._tree["Sonar"].children.values():
beam_group.ds.angle_sensitivity_alongship.attrs[
"long_name"
] = "alongship angle sensitivity of the transducer"
beam_group.ds.angle_sensitivity_athwartship.attrs[
"long_name"
] = "athwartship angle sensitivity of the transducer"
beam_group.ds.angle_offset_alongship.attrs[
"long_name"
] = "electrical alongship angle offset of the transducer"
beam_group.ds.angle_offset_athwartship.attrs[
"long_name"
] = "electrical athwartship angle offset of the transducer"
def _add_comment_to_beam_vars(ed_obj, sensor):
"""
For EK60 and EK80
Add the ``comment`` attribute to the variables
``beamwidth_twoway_alongship/athwartship``,
``angle_offset_alongship/athwartship``,
``angle_sensitivity_alongship/athwartship``,
``angle_athwartship/alongship``,
``beamwidth_twoway_alongship/athwartship``,
``angle_athwartship/alongship``
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor in ["EK60", "EK80"]:
for beam_group in ed_obj._tree["Sonar"].children.values():
beam_group.ds.beamwidth_twoway_alongship.attrs["comment"] = (
"Introduced in echopype for Simrad echosounders to avoid "
"potential confusion with convention definitions. The alongship "
"angle corresponds to the minor angle in SONAR-netCDF4 vers 2. The "
"convention defines one-way transmit or receive beamwidth "
"(beamwidth_receive_minor and beamwidth_transmit_minor), but Simrad "
"echosounders record two-way beamwidth in the data."
)
beam_group.ds.beamwidth_twoway_athwartship.attrs["comment"] = (
"Introduced in echopype for Simrad echosounders to avoid "
"potential confusion with convention definitions. The athwartship "
"angle corresponds to the major angle in SONAR-netCDF4 vers 2. The "
"convention defines one-way transmit or receive beamwidth "
"(beamwidth_receive_major and beamwidth_transmit_major), but Simrad "
"echosounders record two-way beamwidth in the data."
)
beam_group.ds.angle_offset_alongship.attrs["comment"] = (
"Introduced in echopype for Simrad echosounders. The alongship "
"angle corresponds to the minor angle in SONAR-netCDF4 vers 2. "
)
beam_group.ds.angle_offset_athwartship.attrs["comment"] = (
"Introduced in echopype for Simrad echosounders. The athwartship "
"angle corresponds to the major angle in SONAR-netCDF4 vers 2. "
)
beam_group.ds.angle_sensitivity_alongship.attrs[
"comment"
] = beam_group.ds.angle_offset_alongship.attrs["comment"]
beam_group.ds.angle_sensitivity_athwartship.attrs[
"comment"
] = beam_group.ds.angle_offset_athwartship.attrs["comment"]
if "angle_alongship" in beam_group.ds:
beam_group.ds.angle_alongship.attrs[
"comment"
] = beam_group.ds.angle_offset_alongship.attrs["comment"]
if "angle_athwartship" in beam_group.ds:
beam_group.ds.angle_athwartship.attrs[
"comment"
] = beam_group.ds.angle_offset_athwartship.attrs["comment"]
def _beam_groups_to_convention(ed_obj, set_grp_cls):
"""
Adds ``beam`` and ``ping_time`` dimensions to variables
in ``Beam_groupX`` so that they comply with the convention.
For beam groups containing the ``quadrant``,
changes the ``quadrant`` dimension to ``beam``
with string values starting at 1 and sets its attributes
before adding the `beam` and `ping_time` dimensions.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x
set_grp_cls : SetGroupsBase object
The set groups class of the sensor being considered
Notes
-----
The function directly modifies the input EchoData object.
"""
for beam_group in ed_obj._tree["Sonar"].children.values():
if "quadrant" in beam_group.ds:
# change quadrant to beam, assign its values
# to a string starting at 1, and set attributes
beam_group.ds = beam_group.ds.rename({"quadrant": "beam"})
beam_group.ds["beam"] = (beam_group.ds.beam + 1).astype(str)
beam_group.ds.beam.attrs["long_name"] = "Beam name"
set_grp_cls.beam_groups_to_convention(
set_grp_cls,
beam_group.ds,
set_grp_cls.beam_only_names,
set_grp_cls.beam_ping_time_names,
set_grp_cls.ping_time_only_names,
)
def _modify_sonar_group(ed_obj, sensor):
"""
1. Renames ``quadrant`` to ``beam``, sets the
values to strings starting at 1, and sets
attributes, if necessary.
2. Adds ``beam_group`` coordinate to ``Sonar`` group
for all sensors
3. Adds the variable ``beam_group_descr`` to the
``Sonar`` group for all sensors
4. Adds the variable ``sonar_serial_number`` to the
``Sonar`` group and fills it with NaNs (it is missing
information) for the EK80 sensor only.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
The sensor used to create the v0.5.x file.
Notes
-----
The function directly modifies the input EchoData object.
"""
set_groups_cls = SONAR_MODELS[sensor]["set_groups"]
_beam_groups_to_convention(ed_obj, set_groups_cls)
# add beam_group coordinate and beam_group_descr variable
num_beams = len(ed_obj._tree["Sonar"].children.values())
set_groups_cls._beamgroups = set_groups_cls.beamgroups_possible[:num_beams]
beam_groups_vars, beam_groups_coord = set_groups_cls._beam_groups_vars(set_groups_cls)
ed_obj["Sonar"] = ed_obj["Sonar"].assign_coords(beam_groups_coord)
ed_obj["Sonar"] = ed_obj["Sonar"].assign(**beam_groups_vars)
# add sonar_serial_number to EK80 Sonar group
if sensor == "EK80":
ed_obj["Sonar"] = ed_obj["Sonar"].assign(
{
"sonar_serial_number": (
["channel"],
np.full_like(ed_obj["Sonar"].frequency_nominal.values, np.nan),
)
}
)
def _move_transducer_offset_vars(ed_obj, sensor):
"""
Moves transducer_offset_x/y/z from beam groups to Platform
group for EK60 and EK80. If more than one beam group exists,
then the variables are first collected and then moved to
Platform. Additionally, adds ``frequency_nominal`` to
Platform for the EK80 sensor.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor in ["EK60", "EK80"]:
full_transducer_vars = {"x": [], "y": [], "z": []}
# collect transducser_offset_x/y/z from the beam groups
for beam_group in ed_obj._tree["Sonar"].children.values():
for spatial in full_transducer_vars.keys():
full_transducer_vars[spatial].append(beam_group.ds["transducer_offset_" + spatial])
# remove transducer_offset_x/y/z from the beam group
beam_group.ds = beam_group.ds.drop("transducer_offset_" + spatial)
# transfer transducser_offset_x/y/z to Platform
for spatial in full_transducer_vars.keys():
ed_obj["Platform"]["transducer_offset_" + spatial] = xr.concat(
full_transducer_vars[spatial], dim="channel"
)
if sensor == "EK80":
ed_obj["Platform"]["frequency_nominal"] = ed_obj["Vendor"].frequency_nominal.sel(
channel=ed_obj["Platform"].channel
)
def _add_vars_to_platform(ed_obj, sensor):
"""
1.Adds ``MRU_offset_x/y/z``, ``MRU_rotation_x/y/z``, and
``position_offset_x/y/z`` to the ``Platform`` group
for the EK60/EK80/AZFP sensors.
2. Renames ``heave`` to ``vertical_offset`` for the EK60
and EK80.
3. Adds ``transducer_offset_x/y/z``, ``vertical_offset``,
``water_level`` to the ``Platform`` group for the AZFP
sensor only.
4. Adds the coordinate ``time3`` to the ``Platform`` group
for the EK80 sensor only.
5. Adds the variables ``drop_keel_offset(time3)`` (currently in
the attribute), ``drop_keel_offset_is_manual(time3)``, and
``water_level_draft_is_manual(time3)`` to the ``Platform``
group for the EK80 sensor only.
6. Adds the coordinate ``time3`` to the variable ``water_level``
in the ``Platform`` group for the EK80 sensor only.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
ds_tmp = xr.Dataset(
{
var: ([], np.nan, _varattrs["platform_var_default"][var])
for var in [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z",
]
}
)
if sensor == "EK60":
ds_tmp = ds_tmp.expand_dims({"channel": ed_obj["Platform"].channel})
ds_tmp["channel"] = ds_tmp["channel"].assign_attrs(
_varattrs["beam_coord_default"]["channel"]
)
ed_obj["Platform"] = xr.merge([ed_obj["Platform"], ds_tmp])
if sensor != "AZFP": # this variable was missing for AZFP v0.5.x
ed_obj["Platform"] = ed_obj["Platform"].rename({"heave": "vertical_offset"})
if sensor == "EK80":
ed_obj["Platform"]["drop_keel_offset"] = xr.DataArray(
data=[ed_obj["Platform"].attrs["drop_keel_offset"]], dims=["time3"]
)
del ed_obj["Platform"].attrs["drop_keel_offset"]
ed_obj["Platform"]["drop_keel_offset_is_manual"] = xr.DataArray(
data=[np.nan], dims=["time3"]
)
ed_obj["Platform"]["water_level_draft_is_manual"] = xr.DataArray(
data=[np.nan], dims=["time3"]
)
ed_obj["Platform"]["water_level"] = ed_obj["Platform"]["water_level"].expand_dims(
dim=["time3"]
)
ed_obj["Platform"] = ed_obj["Platform"].assign_coords(
{
"time3": (
["time3"],
ed_obj["Environment"].ping_time.values,
{
"axis": "T",
"standard_name": "time",
},
)
}
)
if sensor == "AZFP":
ds_tmp = xr.Dataset(
{
var: ([], np.nan, _varattrs["platform_var_default"][var])
for var in [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
"vertical_offset",
"water_level",
]
}
)
ed_obj["Platform"] = xr.merge([ed_obj["Platform"], ds_tmp])
def _add_vars_coords_to_environment(ed_obj, sensor):
"""
For EK80
1. Adds the length one NaN coordinate ``sound_velocity_profile_depth``
to the ``Environment`` group (this data is missing in v0.5.x).
2. Adds the variables
``sound_velocity_profile(time1, sound_velocity_profile_depth)``,
``sound_velocity_source(time1)``, ``transducer_name(time1)``,
``transducer_sound_speed(time1) to the ``Environment`` group.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor == "EK80":
ed_obj["Environment"]["sound_velocity_source"] = (
["ping_time"],
np.array(len(ed_obj["Environment"].ping_time) * ["None"]),
)
ed_obj["Environment"]["transducer_name"] = (
["ping_time"],
np.array(len(ed_obj["Environment"].ping_time) * ["None"]),
)
ed_obj["Environment"]["transducer_sound_speed"] = (
["ping_time"],
np.array(len(ed_obj["Environment"].ping_time) * [np.nan]),
)
ed_obj["Environment"]["sound_velocity_profile"] = (
["ping_time", "sound_velocity_profile_depth"],
np.nan * np.ones((len(ed_obj["Environment"].ping_time), 1)),
{
"long_name": "sound velocity profile",
"standard_name": "speed_of_sound_in_sea_water",
"units": "m/s",
"valid_min": 0.0,
"comment": "parsed from raw data files as (depth, sound_speed) value pairs",
},
)
ed_obj["Environment"] = ed_obj["Environment"].assign_coords(
{
"sound_velocity_profile_depth": (
["sound_velocity_profile_depth"],
[np.nan],
{
"standard_name": "depth",
"units": "m",
"axis": "Z",
"positive": "down",
"valid_min": 0.0,
},
)
}
)
def _rearrange_azfp_attrs_vars(ed_obj, sensor):
"""
Makes alterations to AZFP variables. Specifically,
variables in ``Beam_group1``.
1. Moves ``tilt_x/y(ping_time)`` to the `Platform` group.
2. Moves ``temperature_counts(ping_time)``,
``tilt_x/y_count(ping_time)``, ``DS(channel)``, ``EL(channel)``,
``TVR(channel)``, ``VTX(channel)``, ``Sv_offset(channel)``,
``number_of_samples_digitized_per_pings(channel)``,
``number_of_digitized_samples_averaged_per_pings(channel)``
to the `Vendor` group:
3. Removes the variable `cos_tilt_mag(ping_time)`
4. Moves the following attributes to the ``Vendor`` group:
``tilt_X_a/b/c/d``, ``tilt_Y_a/b/c/d``,
``temperature_ka/kb/kc/A/B/C``, ``number_of_frequency``,
``number_of_pings_per_burst``, ``average_burst_pings_flag``
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor == "AZFP":
beam_to_plat_vars = ["tilt_x", "tilt_y"]
for var_name in beam_to_plat_vars:
ed_obj["Platform"][var_name] = ed_obj["Sonar/Beam_group1"][var_name]
beam_to_vendor_vars = [
"temperature_counts",
"tilt_x_count",
"tilt_y_count",
"DS",
"EL",
"TVR",
"VTX",
"Sv_offset",
"number_of_samples_digitized_per_pings",
"number_of_digitized_samples_averaged_per_pings",
]
for var_name in beam_to_vendor_vars:
ed_obj["Vendor"][var_name] = ed_obj["Sonar/Beam_group1"][var_name]
beam_to_vendor_attrs = ed_obj["Sonar/Beam_group1"].attrs.copy()
del beam_to_vendor_attrs["beam_mode"]
del beam_to_vendor_attrs["conversion_equation_t"]
for key, val in beam_to_vendor_attrs.items():
ed_obj["Vendor"].attrs[key] = val
del ed_obj["Sonar/Beam_group1"].attrs[key]
ed_obj["Sonar/Beam_group1"] = ed_obj["Sonar/Beam_group1"].drop(
["cos_tilt_mag"] + beam_to_plat_vars + beam_to_vendor_vars
)
def _rename_mru_time_location_time(ed_obj):
"""
Renames location_time to time1 and mru_time to
time2 wherever it occurs.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
for grp_path in ed_obj.group_paths:
if "location_time" in list(ed_obj[grp_path].coords):
# renames location_time to time1
ed_obj[grp_path] = ed_obj[grp_path].rename(name_dict={"location_time": "time1"})
if "mru_time" in list(ed_obj[grp_path].coords):
# renames mru_time to time2
ed_obj[grp_path] = ed_obj[grp_path].rename(name_dict={"mru_time": "time2"})
def _rename_and_add_time_vars_ek60(ed_obj):
"""
1. For EK60's ``Platform`` group this function adds
the variable time3, renames the variable ``water_level``
time coordinate to time3, and changes ``ping_time`` to
``time2`` for the variables ``pitch/roll/vertical_offset``.
2. For EK60's ``Envrionment`` group this function renames
``ping_time`` to ``time1``.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
ed_obj["Platform"]["water_level"] = ed_obj["Platform"]["water_level"].rename(
{"ping_time": "time3"}
)
ed_obj["Platform"] = ed_obj["Platform"].rename({"ping_time": "time2"})
ed_obj["Environment"] = ed_obj["Environment"].rename({"ping_time": "time1"})
ed_obj["Platform"] = ed_obj["Platform"].assign_coords(
{
"time3": (
["time3"],
ed_obj["Platform"].time3.values,
{
"axis": "T",
"standard_name": "time",
},
)
}
)
def _add_time_attrs_in_platform(ed_obj, sensor):
"""
Adds attributes to ``time1``, ``time2``, and
``time3`` in the ``Platform`` group.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if "time1" in ed_obj["Platform"]:
ed_obj["Platform"].time1.attrs[
"comment"
] = "Time coordinate corresponding to NMEA position data."
ed_obj["Platform"].time2.attrs[
"long_name"
] = "Timestamps for platform motion and orientation data"
ed_obj["Platform"].time2.attrs[
"comment"
] = "Time coordinate corresponding to platform motion and orientation data."
if sensor in ["EK80", "EK60"]:
ed_obj["Platform"].time3.attrs[
"long_name"
] = "Timestamps for platform-related sampling environment"
ed_obj["Platform"].time3.attrs[
"comment"
] = "Time coordinate corresponding to platform-related sampling environment."
if sensor == "EK80":
ed_obj["Platform"].time3.attrs["comment"] = (
ed_obj["Platform"].time3.attrs["comment"]
+ " Note that Platform.time3 is the same as Environment.time1."
)
def _add_time_attrs_in_environment(ed_obj, sensor):
"""
Adds attributes to ``time1`` in the ``Environment`` group.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor in ["EK80", "EK60"]:
ed_obj["Environment"].time1.attrs["long_name"] = "Timestamps for NMEA position datagrams"
if sensor == "EK80":
ed_obj["Environment"].time1.attrs["comment"] = (
"Time coordinate corresponding to "
"environmental variables. Note that "
"Platform.time3 is the same as Environment.time1."
)
else:
ed_obj["Environment"].time1.attrs[
"comment"
] = "Time coordinate corresponding to environmental variables."
def _make_time_coords_consistent(ed_obj, sensor):
"""
1. Renames location_time to time1 and mru_time to
time2 wherever it occurs.
2. For EK60 adds and modifies the ``Platform`` group's
time variables.
3. For EK80 renames ``ping_time`` to ``time1`` in the
``Environment`` group.
4. For AZFP renames ``ping_time`` to ``time2`` in the
``Platform`` group and ``ping_time`` to ``time1`` in the
``Environment`` group.
5. Adds time comments to the ``Platform``, ``Platform/NMEA``,
and ``Environment`` groups.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
_rename_mru_time_location_time(ed_obj)
if sensor == "EK60":
_rename_and_add_time_vars_ek60(ed_obj)
if sensor == "EK80":
ed_obj["Environment"] = ed_obj["Environment"].rename({"ping_time": "time1"})
if sensor == "AZFP":
ed_obj["Platform"] = ed_obj["Platform"].rename({"ping_time": "time2"})
ed_obj["Environment"] = ed_obj["Environment"].rename({"ping_time": "time1"})
_add_time_attrs_in_platform(ed_obj, sensor)
_add_time_attrs_in_environment(ed_obj, sensor)
if "Platform/NMEA" in ed_obj.group_paths:
ed_obj["Platform/NMEA"].time1.attrs[
"comment"
] = "Time coordinate corresponding to NMEA sensor data."
def _add_source_filenames_var(ed_obj):
"""
Make the attribute ``src_filenames`` into the
variable ``source_filenames`` in the
``Provenance`` group.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
# this statement only applies for a combined file
if "src_filenames" in ed_obj["Provenance"]:
ed_obj["Provenance"]["source_filenames"] = (
["filenames"],
ed_obj["Provenance"].src_filenames.values,
{"long_name": "Source filenames"},
)
ed_obj["Provenance"].drop("src_filenames")
else:
ed_obj["Provenance"]["source_filenames"] = (
["filenames"],
[ed_obj["Provenance"].attrs["src_filenames"]],
{"long_name": "Source filenames"},
)
del ed_obj["Provenance"].attrs["src_filenames"]
def _rename_vendor_group(ed_obj):
"""
Renames the `Vendor` group to `Vendor_specific` for all
sonar sensors.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
node = ed_obj._tree["Vendor"]
ed_obj._tree["Vendor"].orphan()
ed_obj._tree["Vendor_specific"] = node
def _change_list_attrs_to_str(ed_obj):
"""
If the attribute ``valid_range`` of a variable in
the ``Platform`` group is not a string, turn it
into a string.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
for var in ed_obj["Platform"]:
if "valid_range" in ed_obj["Platform"][var].attrs.keys():
attr_val = ed_obj["Platform"][var].attrs["valid_range"]
if not isinstance(attr_val, str):
ed_obj["Platform"][var].attrs["valid_range"] = f"({attr_val[0]}, {attr_val[1]})"
def _change_vertical_offset_attrs(ed_obj):
"""
Changes the attributes of the variable
``Platform.vertical_offset``.
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
Notes
-----
The function directly modifies the input EchoData object.
"""
if "vertical_offset" in ed_obj["Platform"]:
ed_obj["Platform"]["vertical_offset"].attrs = {
"long_name": "Platform vertical offset from nominal",
"units": "m",
}
def _consistent_sonar_model_attr(ed_obj, sensor):
"""
Brings consistency to the attribute ``sonar_model`` of the
``Sonar`` group amongst the sensors.
1. Changes ``sonar_model`` to "AZFP" for the AZFP sensor.
2. Sets EK60's attribute ``sonar_software_name`` to the
v0.5.x value of ``sonar_model``.
3. Changes ``sonar_model`` to "EK60" for the EK60 sensor.
4. Renames the variable ``Sonar.sonar_model`` to
``Sonar.transducer_name`` for the EK80 sensor.
5. Adds the attribute ``sonar_model`` to the EK80 sensor
and sets it to "EK80".
Parameters
----------
ed_obj : EchoData
EchoData object that was created using echopype version 0.5.x.
sensor : str
Variable specifying the sensor that created the file.
Notes
-----
The function directly modifies the input EchoData object.
"""
if sensor == "AZFP":
ed_obj["Sonar"].attrs["sonar_model"] = "AZFP"
elif sensor == "EK60":
ed_obj["Sonar"].attrs["sonar_software_name"] = ed_obj["Sonar"].attrs["sonar_model"]
ed_obj["Sonar"].attrs["sonar_model"] = "EK60"
elif sensor == "EK80":
ed_obj["Sonar"] = ed_obj["Sonar"].rename({"sonar_model": "transducer_name"})
ed_obj["Sonar"].attrs["sonar_model"] = "EK80"
def convert_v05x_to_v06x(echodata_obj):
"""
This function converts the EchoData structure created in echopype
version 0.5.x to the EchoData structure created in echopype version
0.6.x. Specifically, the following items are completed:
1. Rename the coordinate ``range_bin`` to ``range_sample``
2. Add attributes to `frequency` dimension throughout
all sensors.
3. Map ``Beam`` to ``Sonar/Beam_group1``
4. Map ``Beam_power`` to ``Sonar/Beam_group2``
5. Adds ``beam`` and ``ping_time`` dimensions to
certain variables within the beam groups.
6. Renames ``quadrant`` to ``beam``, sets the
values to strings starting at 1, and sets
attributes, if necessary.
7. Add comment attribute to all _alongship/_athwartship
variables and use two-way beamwidth variables.
8. Adds ``beam_group`` dimension to ``Sonar`` group
9. Adds ``sonar_serial_number``, ``beam_group_name``,
and ``beam_group_descr`` to ``Sonar`` group.
10. Renames ``frequency`` to ``channel`` and adds the
variable ``frequency_nominal`` to every group that
needs it.
11. Move ``transducer_offset_x/y/z`` from beam groups
to the ``Platform`` group (for EK60 and EK80 only).
12. Add variables to the `Platform` group and rename
``heave`` to ``vertical_offset`` (if necessary).
13. Add variables and coordinate to the ``Environment``
group for EK80 only.
14. Move AZFP attributes and variables from ``Beam_group1``
to the ``Vendor`` and ``Platform`` groups. Additionally,
remove the variable ``cos_tilt_mag``, if it exists.
15. Make the names of the time coordinates in the `Platform`
and `Environment` groups consistent and add new the attribute
comment to these time coordinates.
16. Make the attribute ``src_filenames`` into the
variable ``source_filenames`` in the ``Provenance`` group.
17. Rename the `Vendor` group to `Vendor_specific` for all
sonar sensors.
18. Change the attribute ``valid_range`` of variables in
the ``Platform`` group into a string, if it is a numpy array.
19. Change the attributes of the variable
``Platform.vertical_offset``.
20. Bring consistency to the attribute ``sonar_model`` of the
``Sonar`` group.
Parameters
----------
echodata_obj : EchoData
EchoData object that was created using echopype version 0.5.x
Notes
-----
The function directly modifies the input EchoData object.
No actions are taken for AD2CP.
"""
# TODO: put in an appropriate link to the v5 to v6 conversion outline
logger.warning(
"Converting echopype version 0.5.x file to 0.6.0."
" For specific details on how items have been changed,"
" please see the echopype documentation. It is recommended "
"that one creates the file using echopype.open_raw again, "
"rather than relying on this conversion."
)
# get the sensor used to create the v0.5.x file.
sensor = _get_sensor(echodata_obj["Top-level"].keywords)
if sensor == "AD2CP":
pass
else:
_range_bin_to_range_sample(echodata_obj)
_add_attrs_to_freq(echodata_obj)
_reorganize_beam_groups(echodata_obj)
_frequency_to_channel(echodata_obj, sensor)
_change_beam_var_names(echodata_obj, sensor)
_add_comment_to_beam_vars(echodata_obj, sensor)
_modify_sonar_group(echodata_obj, sensor)
_move_transducer_offset_vars(echodata_obj, sensor)
_add_vars_to_platform(echodata_obj, sensor)
_add_vars_coords_to_environment(echodata_obj, sensor)
_rearrange_azfp_attrs_vars(echodata_obj, sensor)
_make_time_coords_consistent(echodata_obj, sensor)
_add_source_filenames_var(echodata_obj)
_change_list_attrs_to_str(echodata_obj)
_change_vertical_offset_attrs(echodata_obj)
_consistent_sonar_model_attr(echodata_obj, sensor)
_rename_vendor_group(echodata_obj)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": 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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,828 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/clean/__init__.py | from .api import estimate_noise, remove_noise
__all__ = [
"estimate_noise",
"remove_noise",
]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,829 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/visualize/plot.py | import matplotlib.pyplot as plt
import matplotlib.cm
import xarray as xr
import numpy as np
from xarray.plot.facetgrid import FacetGrid
from matplotlib.collections import QuadMesh
from typing import Optional, Union, List
from .cm import cmap_d
from ..utils.log import _init_logger
logger = _init_logger(__name__)
def _format_axis_label(axis_variable):
return axis_variable.replace('_', " ").title()
def _set_label(
fg: Union[FacetGrid, QuadMesh, None] = None,
filter_var: str = 'channel',
filter_val: Union[str, int, float, None] = None,
col: Optional[str] = None,
):
props = {'boxstyle': 'square', 'facecolor': 'white', 'alpha': 0.7}
if isinstance(fg, FacetGrid):
text_pos = [0.02, 0.06]
fontsize = 14
if col == 'beam':
if isinstance(filter_val, list) or filter_val is None:
for rl in fg.row_labels:
if rl is not None:
rl.set_text('')
for idx, cl in enumerate(fg.col_labels):
if cl is not None:
cl.set_text(f'Beam {fg.col_names[idx]}')
text_pos = [0.04, 0.06]
fontsize = 10
for idx, ax in enumerate(fg.axes.flat):
name_dicts = fg.name_dicts.flatten()
if filter_var in name_dicts[idx]:
chan = name_dicts[idx][filter_var]
if col == filter_var:
ax.set_title('')
else:
chan = filter_val
ax.set_title(f'Beam {fg.col_names[idx]}')
axtext = chan
if filter_var == 'frequency':
axtext = f"{int(chan / 1000)} kHz"
ax.text(
*text_pos,
axtext,
transform=ax.transAxes,
fontsize=fontsize,
verticalalignment='bottom',
bbox=props,
)
else:
if filter_val is None:
raise ValueError(
f'{filter_var.title()} value is missing for single echogram plotting.'
)
axtext = filter_val
if filter_var == 'frequency':
axtext = f"{int(axtext / 1000)} kHz"
ax = fg.axes
ax.text(
0.02,
0.04,
axtext,
transform=ax.transAxes,
fontsize=13,
verticalalignment='bottom',
bbox=props,
)
plt.title('')
plt.tight_layout()
def _set_plot_defaults(kwargs):
plot_defaults = {
'cmap': 'jet',
'figsize': (15, 10),
'robust': False,
'yincrease': False,
'col_wrap': 1,
}
# Set plot defaults if not passed in kwargs
for k, v in plot_defaults.items():
if k not in kwargs:
kwargs[k] = v
elif k == 'cmap' and k in kwargs:
cmap = kwargs[k]
try:
if cmap in cmap_d:
cmap = f'ep.{cmap}'
kwargs[k] = cmap
matplotlib.cm.get_cmap(cmap)
except:
import cmocean
if cmap.startswith('cmo'):
_, cmap = cmap.split('.')
if cmap in cmocean.cm.cmap_d:
kwargs[k] = f'cmo.{cmap}'
else:
raise ValueError(f"{cmap} is not a valid colormap.")
# Remove extra plotting attributes that should be set
# by echopype devs
exclude_attrs = ['x', 'y', 'col', 'row']
for attr in exclude_attrs:
if attr in kwargs:
logger.warning(f"{attr} in kwargs. Removing.")
kwargs.pop(attr)
return kwargs
def _plot_echogram(
ds: xr.Dataset,
channel: Union[str, List[str], None] = None,
frequency: Union[str, List[str], None] = None,
variable: str = 'backscatter_r',
xaxis: str = 'ping_time',
yaxis: str = 'echo_range',
**kwargs,
) -> Union[FacetGrid, QuadMesh]:
kwargs = _set_plot_defaults(kwargs)
row = None
col = None
filter_var = 'channel'
filter_val = None
# perform frequency filtering
if channel is not None:
if 'channel' not in ds.dims:
raise ValueError("Channel filtering is not available because channel is not a dimension for your dataset!")
ds = ds.sel(channel=channel)
filter_val = channel
elif frequency is not None:
duplicates = False
if 'channel' in ds.dims:
if len(np.unique(ds.frequency_nominal)) != len(ds.frequency_nominal):
duplicates = True
ds = ds.where(ds.frequency_nominal.isin(frequency), drop=True)
else:
if len(np.unique(ds.frequency)) != len(ds.frequency):
duplicates = True
ds = ds.sel(frequency=frequency)
if duplicates:
raise ValueError("Duplicate frequency found, please use channel for filtering.")
filter_val = frequency
filter_var = 'frequency'
if 'backscatter_i' in ds.variables:
col = 'beam'
kwargs.setdefault('figsize', (15, 5))
kwargs.update(
{
'col_wrap': None,
}
)
filtered_ds = np.abs(ds.backscatter_r + 1j * ds.backscatter_i)
else:
filtered_ds = ds[variable]
if 'beam' in filtered_ds.dims:
filtered_ds = filtered_ds.isel(beam=0).drop('beam')
if 'channel' in filtered_ds.dims and frequency is not None:
filtered_ds = filtered_ds.assign_coords({'frequency': ds.frequency_nominal})
filtered_ds = filtered_ds.swap_dims({'channel': 'frequency'})
if filtered_ds.frequency.size == 1:
filtered_ds = filtered_ds.isel(frequency=0)
# figure out channel/frequency size
# to determine plotting method
if filtered_ds[filter_var].size > 1:
if col is None:
col = filter_var
else:
row = filter_var
filtered_ds[xaxis].attrs = {
'long_name': filtered_ds[xaxis].attrs.get(
'long_name', _format_axis_label(xaxis)
),
'units': filtered_ds[xaxis].attrs.get('units', ''),
}
filtered_ds[yaxis].attrs = {
'long_name': filtered_ds[yaxis].attrs.get(
'long_name', _format_axis_label(yaxis)
),
'units': filtered_ds[yaxis].attrs.get('units', ''),
}
plots = []
if not filtered_ds[filter_var].shape:
if (
np.any(filtered_ds.isnull()).values == np.array(True)
and 'echo_range' in filtered_ds.coords
and 'range_sample' in filtered_ds.dims
and variable in ['backscatter_r', 'Sv']
):
# Handle the nans for echodata and Sv
filtered_ds = filtered_ds.sel(
ping_time=filtered_ds.echo_range.dropna(dim='ping_time', how='all').ping_time
)
filtered_ds = filtered_ds.sel(
range_sample=filtered_ds.echo_range.dropna(dim='range_sample').range_sample
)
plot = filtered_ds.plot.pcolormesh(
x=xaxis,
y=yaxis,
col=col,
row=row,
**kwargs,
)
_set_label(plot, filter_var=filter_var, filter_val=filter_val, col=col)
plots.append(plot)
else:
# Scale plots
num_chan = len(filtered_ds[filter_var])
chan_scaling = (-0.06, -0.16)
figsize_scale = tuple(
[1 + (scale * num_chan) for scale in chan_scaling]
)
new_size = tuple(
[
size * figsize_scale[idx]
for idx, size in enumerate(kwargs.get('figsize'))
]
)
kwargs.update({'figsize': new_size})
for f in filtered_ds[filter_var]:
d = filtered_ds[filtered_ds[filter_var] == f.values]
if (
np.any(d.isnull()).values == np.array(True)
and 'echo_range' in d.coords
and 'range_sample' in d.dims
and variable in ['backscatter_r', 'Sv']
):
# Handle the nans for echodata and Sv
d = d.sel(
ping_time=d.echo_range.dropna(dim='ping_time', how='all').ping_time
)
d = d.sel(
range_sample=d.echo_range.dropna(dim='range_sample').range_sample
)
plot = d.plot.pcolormesh(
x=xaxis,
y=yaxis,
col=col,
row=row,
**kwargs,
)
_set_label(plot, filter_var=filter_var, filter_val=filter_val, col=col)
plots.append(plot)
return plots
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,830 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/commongrid/__init__.py | from .api import compute_MVBS, compute_MVBS_index_binning
__all__ = [
"compute_MVBS",
"compute_MVBS_index_binning",
]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,831 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parsed_to_zarr.py | import secrets
import sys
from pathlib import Path
from typing import List, Tuple, Union
import more_itertools as miter
import numpy as np
import pandas as pd
import zarr
from ..utils.io import ECHOPYPE_DIR, check_file_permissions
class Parsed2Zarr:
"""
This class contains functions that facilitate
the writing of a parsed file to a zarr file.
Additionally, it contains useful information,
such as names of array groups and their paths.
"""
def __init__(self, parser_obj):
self.temp_zarr_dir = None
self.zarr_file_name = None
self.store = None
self.zarr_root = None
self.parser_obj = parser_obj # parser object ParseEK60/ParseEK80/etc.
def _create_zarr_info(self):
"""
Creates the temporary directory for zarr
storage, zarr file name, zarr store, and
the root group of the zarr store.
"""
# get current working directory
current_dir = Path.cwd()
# Check permission of cwd, raise exception if no permission
check_file_permissions(current_dir)
# construct temporary directory that will hold the zarr file
out_dir = current_dir / ECHOPYPE_DIR / "temp_output" / "parsed2zarr_temp_files"
if not out_dir.exists():
out_dir.mkdir(parents=True)
# establish temporary directory we will write zarr files to
self.temp_zarr_dir = str(out_dir)
# create zarr store name
zarr_file_name = str(out_dir / secrets.token_hex(16)) + ".zarr"
# attempt to find different zarr_file_name, if it already exists
count = 0
while Path(zarr_file_name).exists() and count < 10:
# generate new zarr_file_name
zarr_file_name = str(out_dir / secrets.token_hex(16)) + ".zarr"
count += 1
# error out if we are unable to get a unique name, else assign name to class variable
if (count == 10) and Path(zarr_file_name).exists():
raise RuntimeError("Unable to construct an unused zarr file name for Parsed2Zarr!")
else:
self.zarr_file_name = zarr_file_name
# create zarr store and zarr group we want to write to
self.store = zarr.DirectoryStore(self.zarr_file_name)
self.zarr_root = zarr.group(store=self.store, overwrite=True)
def _close_store(self):
"""properly closes zarr store"""
# consolidate metadata and close zarr store
zarr.consolidate_metadata(self.store)
self.store.close()
@staticmethod
def set_multi_index(
pd_obj: Union[pd.Series, pd.DataFrame], unique_dims: List[pd.Index]
) -> Union[pd.Series, pd.DataFrame]:
"""
Sets a multi-index from the product of the unique
dimension values on a series and then
returns it.
Parameters
----------
pd_obj : Union[pd.Series, pd.DataFrame]
Series or DataFrame that needs its multi-index modified.
unique_dims : List[pd.Index]
List where the elements are the unique values
of the index.
Returns
-------
Union[pd.Series, pd.DataFrame]
``pd_obj`` with product multi-index
Notes
-----
By setting the multiindex, this method fills (or pads)
missing dimension values.
"""
multi_index = pd.MultiIndex.from_product(unique_dims)
# set product multi-index i.e. a preliminary padding of the df
return pd_obj.reindex(multi_index, fill_value=np.nan)
@staticmethod
def get_max_elem_shape(pd_series: pd.Series) -> np.ndarray:
"""
Returns the maximum element shape for a
Series that has array elements
Parameters
----------
pd_series: pd.Series
Series with array elements
Returns
-------
np.ndarray
The maximum element shape
"""
all_shapes = pd_series.apply(
lambda x: np.array(x.shape) if isinstance(x, np.ndarray) else None
).dropna()
all_dims = np.vstack(all_shapes.to_list())
return all_dims.max(axis=0)
def get_col_info(
self, pd_series: pd.Series, time_name: str, is_array: bool, max_mb: int
) -> Tuple[int, list]:
"""
Provides the maximum number of times needed to
fill at most `max_mb` MB of memory and the
shape of each chunk.
Parameters
----------
pd_series : pd.Series
Series representing a column of the datagram df
time_name : str
The name of the index corresponding to time
is_array : bool
Specifies if we are working with a column that
has arrays
max_mb : int
Maximum MB allowed for each chunk
Returns
-------
max_num_times : int
The number of times needed to fill at most
`max_mb` MB of memory.
chunk_shape : list
The shape of the chunk.
Notes
-----
This function assumes that our df has 2 indices and
``time_name`` is one of them.
For ``chunk_shape`` the first element corresponds to time
and this element will be filled later, thus, it is set
to None here. The shape of chunk is of the form:
``[None, num_index_2, max_element_shape]`` if we have an
array column and ``[None, num_index_2]`` if we have a
column that does not contain an array.
"""
multi_ind_names = list(pd_series.index.names)
if len(multi_ind_names) > 2:
raise NotImplementedError("series contains more than 2 indices!")
multi_ind_names.remove(time_name) # allows us to infer the other index name
# get maximum dimension of column element
if is_array:
max_element_shape = self.get_max_elem_shape(pd_series)
else:
max_element_shape = 1
# bytes required to hold one element of the column
# TODO: this assumes we are holding floats (the 8 value), generalize it
elem_bytes = max_element_shape.prod(axis=0) * 8
# the number of unique elements in the second index
index_2_name = multi_ind_names[0]
num_index_2 = len(pd_series.index.unique(index_2_name))
bytes_per_time = num_index_2 * elem_bytes
mb_per_time = bytes_per_time / 1e6
# The maximum number of times needed to fill at most `max_mb` MB of memory
max_num_times = max_mb // mb_per_time
# create form of chunk shape
if isinstance(max_element_shape, np.ndarray):
chunk_shape = [None, num_index_2, max_element_shape]
else:
chunk_shape = [None, num_index_2]
return max_num_times, chunk_shape
@staticmethod
def get_np_chunk(
series_chunk: pd.Series, chunk_shape: list, nan_array: np.ndarray
) -> np.ndarray:
"""
Manipulates the ``series_chunk`` values into the
correct shape that can then be written to a
zarr array.
Parameters
----------
series_chunk : pd.Series
A chunk of the dataframe column
chunk_shape : list
Specifies what shape the numpy chunk
should be reshaped to
nan_array : np.ndarray
An array filled with NaNs that has the
maximum length of a column's element.
This value is used to pad empty elements.
Returns
-------
np_chunk : np.ndarray
Final form of series_chunk that can be
written to a zarr array
"""
if isinstance(nan_array, np.ndarray):
# appropriately pad elements of series_chunk, if needed
padded_elements = []
for elm in series_chunk.to_list():
if isinstance(elm, np.ndarray):
# TODO: ideally this would take place in the parser, do this
elm = elm.astype(np.float64)
# amount of padding to add to each axis
padding_amount = chunk_shape[2] - elm.shape
# create np.pad pad_width
pad_width = [(0, i) for i in padding_amount]
padded_array = np.pad(elm, pad_width, "constant", constant_values=np.nan)
padded_elements.append(padded_array)
else:
padded_elements.append(nan_array)
np_chunk = np.concatenate(padded_elements, axis=0, dtype=np.float64)
# reshape chunk to the appropriate size
full_shape = chunk_shape[:2] + list(chunk_shape[2])
np_chunk = np_chunk.reshape(full_shape)
else:
np_chunk = series_chunk.to_numpy().reshape(chunk_shape)
return np_chunk
def write_chunks(
self,
pd_series: pd.Series,
zarr_grp: zarr.group,
is_array: bool,
chunks: list,
chunk_shape: list,
) -> None:
"""
Writes ``pd_series`` to ``zarr_grp`` as a zarr array
with name ``pd_series.name``, using the specified chunks.
Parameters
----------
pd_series : pd:Series
Series representing a column of the datagram df
zarr_grp: zarr.group
Zarr group that we should write the zarr array to
is_array : bool
True if ``pd_series`` has elements that are arrays,
False otherwise
chunks: list
A list where each element corresponds to a list of
index values that should be chosen for the chunk.
For example, if we are chunking along time, ``chunks``
would have the form:
``[['2004-09-09 16:19:06.059000', ..., '2004-09-09 16:19:06.746000'],
['2004-09-09 16:19:07.434000', ..., '2004-09-09 16:19:08.121000']]``.
chunk_shape: list
A list where each element specifies the shape of the
zarr chunk for a given element of ``chunks``
"""
if is_array:
# nan array used in padding of elements
nan_array = np.empty(chunk_shape[2], dtype=np.float64)
nan_array[:] = np.nan
else:
nan_array = np.empty(1, dtype=np.float64)
# obtain the number of times for each chunk
chunk_len = [len(i) for i in chunks]
max_chunk_len = max(chunk_len)
zarr_chunk_shape = chunk_shape[:2] + list(chunk_shape[2])
zarr_chunk_shape[0] = max_chunk_len
# obtain initial chunk in the proper form
series_chunk = pd_series.loc[chunks[0]]
chunk_shape[0] = chunk_len[0]
np_chunk = self.get_np_chunk(series_chunk, chunk_shape, nan_array)
# create array in zarr_grp using initial chunk
full_array = zarr_grp.array(
name=pd_series.name,
data=np_chunk,
chunks=zarr_chunk_shape,
dtype="f8",
fill_value="NaN",
)
# append each chunk to full_array
for i, chunk in enumerate(chunks[1:], start=1):
series_chunk = pd_series.loc[chunk]
chunk_shape[0] = chunk_len[i]
np_chunk = self.get_np_chunk(series_chunk, chunk_shape, nan_array)
full_array.append(np_chunk)
def write_df_column(
self,
pd_series: pd.Series,
zarr_grp: zarr.group,
is_array: bool,
unique_time_ind: pd.Index,
max_mb: int = 100,
) -> None:
"""
Obtains the appropriate information needed
to determine the chunks of a column and
then calls the function that writes a
column to a zarr array.
Parameters
----------
pd_series: pd.Series
Series with product multi-index and elements that
are either an array or none of the elements are arrays.
zarr_grp: zarr.group
Zarr group that we should write the zarr array to
is_array : bool
True if ``pd_series`` is such that the elements of every
column are arrays, False otherwise
unique_time_ind : pd.Index
The unique time index values of ``pd_series``
max_mb : int
Maximum MB allowed for each chunk
Notes
-----
This assumes that our pd_series has at most 2 indices.
"""
if len(pd_series.index.names) > 2:
raise NotImplementedError("series contains more than 2 indices!")
# For a column, obtain the maximum amount of times needed for
# each chunk and the associated form for the shape of the chunks
max_num_times, chunk_shape = self.get_col_info(
pd_series, unique_time_ind.name, is_array=is_array, max_mb=max_mb
)
# evenly chunk unique times so that the smallest and largest
# chunk differ by at most 1 element
chunks = list(miter.chunked_even(unique_time_ind, max_num_times))
self.write_chunks(pd_series, zarr_grp, is_array, chunks, chunk_shape)
def _get_zarr_dgrams_size(self) -> int:
"""
Returns the size in bytes of the list of zarr
datagrams.
"""
size = 0
for i in self.parser_obj.zarr_datagrams:
size += sum([sys.getsizeof(val) for key, val in i.items()])
return size
def array_series_bytes(self, pd_series: pd.Series, n_rows: int) -> int:
"""
Determines the amount of bytes required for a
series with array elements, for ``n_rows``.
Parameters
----------
pd_series: pd.Series
Series with array elements
n_rows: int
The number of rows with array elements
Returns
-------
The amount of bytes required to hold data
"""
# the number of bytes required to hold 1 element of series
# Note: this assumes that we are holding floats
pow_bytes = self.get_max_elem_shape(pd_series).prod(axis=0) * 8
# total memory required for series data
return n_rows * pow_bytes
def whether_write_to_zarr(self, **kwargs) -> None:
"""
Determines if the zarr data provided will expand
into a form that is larger than a percentage of
the total physical RAM.
"""
pass
def datagram_to_zarr(self, **kwargs) -> None:
"""
Facilitates the conversion of a list of
datagrams to a form that can be written
to a zarr store.
"""
pass
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,832 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/calibrate/__init__.py | from .api import compute_Sv, compute_TS
__all__ = ["compute_Sv", "compute_TS"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,833 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/convention/utils.py | from ..convention import sonarnetcdf_1
def _get_sonar_groups():
"""Utility to reorder convention file by the paths"""
group_mapping = sonarnetcdf_1.yaml_dict["groups"]
sonar_groups = {}
for k, v in group_mapping.items():
group_path = v.get("ep_group")
if any(group_path == p for p in [None, "/"]):
group_path = "Top-level"
sonar_groups.setdefault(
group_path,
{"description": v.get("description"), "name": v.get("name")},
)
return sonar_groups
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,834 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/echodata/test_echodata.py | from textwrap import dedent
import os
import fsspec
from pathlib import Path
import shutil
from datatree import DataTree
from zarr.errors import GroupNotFoundError
import echopype
from echopype.calibrate.env_params_old import EnvParams
from echopype.echodata import EchoData
from echopype import open_converted
from echopype.calibrate.calibrate_ek import CalibrateEK60, CalibrateEK80
import pytest
import xarray as xr
import numpy as np
from utils import get_mock_echodata, check_consolidated
@pytest.fixture(scope="module")
def single_ek60_zarr(test_path):
return (
test_path['EK60'] / "ncei-wcsd" / "Summer2017-D20170615-T190214__NEW.zarr"
)
@pytest.fixture(
params=[
single_ek60_zarr,
(str, "ncei-wcsd", "Summer2017-D20170615-T190214.zarr"),
(None, "ncei-wcsd", "Summer2017-D20170615-T190214__NEW.nc"),
"s3://data/ek60/ncei-wcsd/Summer2017-D20170615-T190214.nc",
"http://localhost:8080/data/ek60/ncei-wcsd/Summer2017-D20170615-T190214.zarr",
"s3://data/ek60/ncei-wcsd/Summer2017-D20170615-T190214.zarr",
fsspec.get_mapper(
"s3://data/ek60/ncei-wcsd/Summer2017-D20170615-T190214.zarr",
**dict(
client_kwargs=dict(endpoint_url="http://localhost:9000/"),
key="minioadmin",
secret="minioadmin",
),
),
],
ids=[
"ek60_zarr_path",
"ek60_zarr_path_string",
"ek60_netcdf_path",
"ek60_netcdf_s3_string",
"ek60_zarr_http_string",
"ek60_zarr_s3_string",
"ek60_zarr_s3_FSMap",
],
)
def ek60_converted_zarr(request, test_path):
if isinstance(request.param, tuple):
desired_type, *paths = request.param
if desired_type is not None:
return desired_type(test_path['EK60'].joinpath(*paths))
else:
return test_path['EK60'].joinpath(*paths)
else:
return request.param
@pytest.fixture(
params=[
(
("EK60", "ncei-wcsd", "Summer2017-D20170615-T190214.raw"),
"EK60",
None,
None,
"CW",
"power",
),
(
("EK80_NEW", "D20211004-T233354.raw"),
"EK80",
None,
None,
"CW",
"power",
),
(
("EK80_NEW", "echopype-test-D20211004-T235930.raw"),
"EK80",
None,
None,
"BB",
"complex",
),
(
("EK80_NEW", "D20211004-T233115.raw"),
"EK80",
None,
None,
"CW",
"complex",
),
(
("ES70", "D20151202-T020259.raw"),
"ES70",
None,
None,
None,
None,
),
(
("AZFP", "ooi", "17032923.01A"),
"AZFP",
("AZFP", "ooi", "17032922.XML"),
"Sv",
None,
None,
),
(
("AZFP", "ooi", "17032923.01A"),
"AZFP",
("AZFP", "ooi", "17032922.XML"),
"TS",
None,
None,
),
(
("AD2CP", "raw", "090", "rawtest.090.00001.ad2cp"),
"AD2CP",
None,
None,
None,
None,
),
],
ids=[
"ek60_cw_power",
"ek80_cw_power",
"ek80_bb_complex",
"ek80_cw_complex",
"es70",
"azfp_sv",
"azfp_sp",
"ad2cp",
],
)
def compute_range_samples(request, test_path):
(
filepath,
sonar_model,
azfp_xml_path,
azfp_cal_type,
ek_waveform_mode,
ek_encode_mode,
) = request.param
if sonar_model.lower() == 'es70':
pytest.xfail(
reason="Not supported at the moment",
)
path_model, *paths = filepath
filepath = test_path[path_model].joinpath(*paths)
if azfp_xml_path is not None:
path_model, *paths = azfp_xml_path
azfp_xml_path = test_path[path_model].joinpath(*paths)
return (
filepath,
sonar_model,
azfp_xml_path,
azfp_cal_type,
ek_waveform_mode,
ek_encode_mode,
)
@pytest.fixture(
params=[
{
"path_model": "EK60",
"raw_path": "Winter2017-D20170115-T150122.raw",
},
{
"path_model": "EK80",
"raw_path": "D20170912-T234910.raw",
},
],
ids=[
"ek60_winter2017",
"ek80_summer2017",
],
)
def range_check_files(request, test_path):
return (
request.param["path_model"],
test_path[request.param["path_model"]].joinpath(request.param['raw_path'])
)
class TestEchoData:
expected_groups = (
'Top-level',
'Environment',
'Platform',
'Platform/NMEA',
'Provenance',
'Sonar',
'Sonar/Beam_group1',
'Vendor_specific',
)
@pytest.fixture(scope="class")
def mock_echodata(self):
return get_mock_echodata()
@pytest.fixture(scope="class")
def converted_zarr(self, single_ek60_zarr):
return single_ek60_zarr
def create_ed(self, converted_raw_path):
return EchoData.from_file(converted_raw_path=converted_raw_path)
def test_constructor(self, converted_zarr):
ed = self.create_ed(converted_zarr)
assert ed.sonar_model == 'EK60'
assert ed.converted_raw_path == converted_zarr
assert ed.storage_options == {}
for group in self.expected_groups:
assert isinstance(ed[group], xr.Dataset)
def test_group_paths(self, converted_zarr):
ed = self.create_ed(converted_zarr)
assert ed.group_paths == self.expected_groups
def test_nbytes(self, converted_zarr):
ed = self.create_ed(converted_zarr)
assert isinstance(ed.nbytes, float)
assert ed.nbytes == 4688692.0
def test_repr(self, converted_zarr):
zarr_path_string = str(converted_zarr.absolute())
expected_repr = dedent(
f"""\
<EchoData: standardized raw data from {zarr_path_string}>
Top-level: contains metadata about the SONAR-netCDF4 file format.
├── Environment: contains information relevant to acoustic propagation through water.
├── Platform: contains information about the platform on which the sonar is installed.
│ └── NMEA: contains information specific to the NMEA protocol.
├── Provenance: contains metadata about how the SONAR-netCDF4 version of the data were obtained.
├── Sonar: contains sonar system metadata and sonar beam groups.
│ └── Beam_group1: contains backscatter power (uncalibrated) and other beam or channel-specific data, including split-beam angle data when they exist.
└── Vendor_specific: contains vendor-specific information about the sonar and the data."""
)
ed = self.create_ed(converted_raw_path=converted_zarr)
actual = "\n".join(x.rstrip() for x in repr(ed).split("\n"))
assert expected_repr == actual
def test_repr_html(self, converted_zarr):
zarr_path_string = str(converted_zarr.absolute())
ed = self.create_ed(converted_raw_path=converted_zarr)
assert hasattr(ed, "_repr_html_")
html_repr = ed._repr_html_().strip()
assert (
f"""<div class="xr-obj-type">EchoData: standardized raw data from {zarr_path_string}</div>"""
in html_repr
)
with xr.set_options(display_style="text"):
html_fallback = ed._repr_html_().strip()
assert html_fallback.startswith(
"<pre><EchoData"
) and html_fallback.endswith("</pre>")
def test_setattr(self, converted_zarr):
sample_data = xr.Dataset({"x": [0, 0, 0]})
sample_data2 = xr.Dataset({"y": [0, 0, 0]})
ed = self.create_ed(converted_raw_path=converted_zarr)
current_ed_beam = ed["Sonar/Beam_group1"]
current_ed_top = ed['Top-level']
ed["Sonar/Beam_group1"] = sample_data
ed['Top-level'] = sample_data2
assert ed["Sonar/Beam_group1"].equals(sample_data) is True
assert ed["Sonar/Beam_group1"].equals(current_ed_beam) is False
assert ed['Top-level'].equals(sample_data2) is True
assert ed['Top-level'].equals(current_ed_top) is False
def test_getitem(self, converted_zarr):
ed = self.create_ed(converted_raw_path=converted_zarr)
beam = ed['Sonar/Beam_group1']
assert isinstance(beam, xr.Dataset)
assert ed['MyGroup'] is None
ed._tree = None
try:
ed['Sonar']
except Exception as e:
assert isinstance(e, ValueError)
def test_setitem(self, converted_zarr):
ed = self.create_ed(converted_raw_path=converted_zarr)
ed['Sonar/Beam_group1'] = ed['Sonar/Beam_group1'].rename({'beam': 'beam_newname'})
assert sorted(ed['Sonar/Beam_group1'].dims.keys()) == ['beam_newname', 'channel', 'ping_time', 'range_sample']
try:
ed['SomeRandomGroup'] = 'Testing value'
except Exception as e:
assert isinstance(e, GroupNotFoundError)
def test_get_dataset(self, converted_zarr):
ed = self.create_ed(converted_raw_path=converted_zarr)
node = DataTree()
result = ed._EchoData__get_dataset(node)
ed_node = ed._tree['Sonar']
ed_result = ed._EchoData__get_dataset(ed_node)
assert result is None
assert isinstance(ed_result, xr.Dataset)
@pytest.mark.parametrize("consolidated", [True, False])
def test_to_zarr_consolidated(self, mock_echodata, consolidated):
"""
Tests to_zarr consolidation. Currently, this test uses a mock EchoData object that only
has attributes. The consolidated flag provided will be used in every to_zarr call (which
is used to write each EchoData group to zarr_path).
"""
zarr_path = Path('test.zarr')
mock_echodata.to_zarr(str(zarr_path), consolidated=consolidated, overwrite=True)
check = True if consolidated else False
zmeta_path = zarr_path / ".zmetadata"
assert zmeta_path.exists() is check
if check is True:
check_consolidated(mock_echodata, zmeta_path)
# clean up the zarr file
shutil.rmtree(zarr_path)
def test_open_converted(ek60_converted_zarr, minio_bucket): # noqa
def _check_path(zarr_path):
storage_options = {}
if zarr_path.startswith("s3://"):
storage_options = dict(
client_kwargs=dict(endpoint_url="http://localhost:9000/"),
key="minioadmin",
secret="minioadmin",
)
return storage_options
storage_options = {}
if not isinstance(ek60_converted_zarr, fsspec.FSMap):
storage_options = _check_path(str(ek60_converted_zarr))
try:
ed = open_converted(
ek60_converted_zarr, storage_options=storage_options
)
assert isinstance(ed, EchoData) is True
except Exception as e:
if (
isinstance(ek60_converted_zarr, str)
and ek60_converted_zarr.startswith("s3://")
and ek60_converted_zarr.endswith(".nc")
):
assert isinstance(e, ValueError) is True
# def test_compute_range(compute_range_samples):
# (
# filepath,
# sonar_model,
# azfp_xml_path,
# azfp_cal_type,
# ek_waveform_mode,
# ek_encode_mode,
# ) = compute_range_samples
# ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path)
# rng = np.random.default_rng(0)
# stationary_env_params = EnvParams(
# xr.Dataset(
# data_vars={
# "pressure": ("time3", np.arange(50)),
# "salinity": ("time3", np.arange(50)),
# "temperature": ("time3", np.arange(50)),
# },
# coords={
# "time3": np.arange("2017-06-20T01:00", "2017-06-20T01:25", np.timedelta64(30, "s"), dtype="datetime64[ns]")
# }
# ),
# data_kind="stationary"
# )
# if "time3" in ed["Platform"] and sonar_model != "AD2CP":
# ed.compute_range(stationary_env_params, azfp_cal_type, ek_waveform_mode)
# else:
# try:
# ed.compute_range(stationary_env_params, ek_waveform_mode="CW", azfp_cal_type="Sv")
# except ValueError:
# pass
# else:
# raise AssertionError
# mobile_env_params = EnvParams(
# xr.Dataset(
# data_vars={
# "pressure": ("time", np.arange(100)),
# "salinity": ("time", np.arange(100)),
# "temperature": ("time", np.arange(100)),
# },
# coords={
# "latitude": ("time", rng.random(size=100) + 44),
# "longitude": ("time", rng.random(size=100) - 125),
# }
# ),
# data_kind="mobile"
# )
# if "latitude" in ed["Platform"] and "longitude" in ed["Platform"] and sonar_model != "AD2CP" and not np.isnan(ed["Platform"]["time1"]).all():
# ed.compute_range(mobile_env_params, azfp_cal_type, ek_waveform_mode)
# else:
# try:
# ed.compute_range(mobile_env_params, ek_waveform_mode="CW", azfp_cal_type="Sv")
# except ValueError:
# pass
# else:
# raise AssertionError
# env_params = {"sound_speed": 343}
# if sonar_model == "AD2CP":
# try:
# ed.compute_range(
# env_params, ek_waveform_mode="CW", azfp_cal_type="Sv"
# )
# except ValueError:
# pass # AD2CP is not currently supported in ed.compute_range
# else:
# raise AssertionError
# else:
# echo_range = ed.compute_range(
# env_params,
# azfp_cal_type,
# ek_waveform_mode,
# )
# assert isinstance(echo_range, xr.DataArray)
def test_nan_range_entries(range_check_files):
sonar_model, ek_file = range_check_files
echodata = echopype.open_raw(ek_file, sonar_model=sonar_model)
if sonar_model == "EK80":
ds_Sv = echopype.calibrate.compute_Sv(echodata, waveform_mode='BB', encode_mode='complex')
cal_obj = CalibrateEK80(
echodata, env_params=None, cal_params=None, ecs_file=None, waveform_mode="BB", encode_mode="complex",
)
range_output = cal_obj.range_meter
# broadband complex data EK80 file: always need to drop "beam" dimension
nan_locs_backscatter_r = ~echodata["Sonar/Beam_group1"].backscatter_r.isel(beam=0).drop("beam").isnull()
else:
# EK60 file does not need dropping "beam" dimension
ds_Sv = echopype.calibrate.compute_Sv(echodata)
cal_obj = CalibrateEK60(echodata, env_params={}, cal_params=None, ecs_file=None)
range_output = cal_obj.range_meter
nan_locs_backscatter_r = ~echodata["Sonar/Beam_group1"].backscatter_r.isnull()
nan_locs_Sv_range = ~ds_Sv.echo_range.isnull()
nan_locs_range = ~range_output.isnull()
assert xr.Dataset.equals(nan_locs_backscatter_r, nan_locs_range)
assert xr.Dataset.equals(nan_locs_backscatter_r, nan_locs_Sv_range)
@pytest.mark.parametrize(
["ext_type", "sonar_model", "variable_mappings", "path_model", "raw_path", "platform_data"],
[
(
"external-trajectory",
"EK80",
# variable_mappings dictionary as {Platform_var_name: external-data-var-name}
{"pitch": "PITCH", "roll": "ROLL", "longitude": "longitude", "latitude": "latitude"},
"EK80",
(
"saildrone",
"SD2019_WCS_v05-Phase0-D20190617-T125959-0.raw",
),
(
"saildrone",
"saildrone-gen_5-fisheries-acoustics-code-sprint-sd1039-20190617T130000-20190618T125959-1_hz-v1.1595357449818.nc", #noqa
),
),
(
"fixed-location",
"EK60",
# variable_mappings dictionary as {Platform_var_name: external-data-var-name}
{"longitude": "longitude", "latitude": "latitude"},
"EK60",
(
"ooi",
"CE02SHBP-MJ01C-07-ZPLSCB101_OOI-D20191201-T000000.raw"
),
(-100.0, -50.0),
),
],
)
def test_update_platform(
ext_type,
sonar_model,
variable_mappings,
path_model,
raw_path,
platform_data,
test_path
):
raw_file = test_path[path_model] / raw_path[0] / raw_path[1]
ed = echopype.open_raw(raw_file, sonar_model=sonar_model)
# Test that the variables in Platform are all empty (nan)
for variable in variable_mappings.keys():
assert np.isnan(ed["Platform"][variable].values).all()
# Prepare the external data
if ext_type == "external-trajectory":
extra_platform_data_file_name = platform_data[1]
extra_platform_data = xr.open_dataset(
test_path[path_model] / platform_data[0] / extra_platform_data_file_name
)
elif ext_type == "fixed-location":
extra_platform_data_file_name = None
extra_platform_data = xr.Dataset(
{
"longitude": (["time"], np.array([float(platform_data[0])])),
"latitude": (["time"], np.array([float(platform_data[1])])),
},
coords={
"time": (["time"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()]))
},
)
# Run update_platform
ed.update_platform(
extra_platform_data,
variable_mappings=variable_mappings,
extra_platform_data_file_name=extra_platform_data_file_name,
)
for variable in variable_mappings.keys():
assert not np.isnan(ed["Platform"][variable].values).all()
# times have max interval of 2s
# check times are > min(ed["Sonar/Beam_group1"]["ping_time"]) - 2s
assert (
ed["Platform"]["time3"]
> ed["Sonar/Beam_group1"]["ping_time"].min() - np.timedelta64(2, "s")
).all()
# check there is only 1 time < min(ed["Sonar/Beam_group1"]["ping_time"])
assert (
np.count_nonzero(
ed["Platform"]["time3"] < ed["Sonar/Beam_group1"]["ping_time"].min()
)
<= 1
)
# check times are < max(ed["Sonar/Beam_group1"]["ping_time"]) + 2s
assert (
ed["Platform"]["time3"]
< ed["Sonar/Beam_group1"]["ping_time"].max() + np.timedelta64(2, "s")
).all()
# check there is only 1 time > max(ed["Sonar/Beam_group1"]["ping_time"])
assert (
np.count_nonzero(
ed["Platform"]["time3"] > ed["Sonar/Beam_group1"]["ping_time"].max()
)
<= 1
)
def test_update_platform_multidim(test_path):
raw_file = test_path["EK60"] / "ooi" / "CE02SHBP-MJ01C-07-ZPLSCB101_OOI-D20191201-T000000.raw"
ed = echopype.open_raw(raw_file, sonar_model="EK60")
extra_platform_data = xr.Dataset(
{
"lon": (["time"], np.array([-100.0])),
"lat": (["time"], np.array([-50.0])),
"pitch": (["time_pitch"], np.array([0.1])),
"waterlevel": ([], float(10)),
},
coords={
"time": (["time"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()])),
"time_pitch": (
["time_pitch"],
# Adding a time delta is not necessary, but it may be handy if we later
# want to expand the scope of this test
np.array([ed['Sonar/Beam_group1'].ping_time.values.min()]) + np.timedelta64(5, "s")
)
},
)
platform_preexisting_dims = ed["Platform"].dims
variable_mappings = {
"longitude": "lon",
"latitude": "lat",
"pitch": "pitch",
"water_level": "waterlevel"
}
ed.update_platform(extra_platform_data, variable_mappings=variable_mappings)
# Updated variables are not all nan
for variable in variable_mappings.keys():
assert not np.isnan(ed["Platform"][variable].values).all()
# Number of dimensions in Platform group and addition of time3 and time4
assert len(ed["Platform"].dims) == len(platform_preexisting_dims) + 2
assert "time3" in ed["Platform"].dims
assert "time4" in ed["Platform"].dims
# Dimension assignment
assert ed["Platform"]["longitude"].dims[0] == ed["Platform"]["latitude"].dims[0]
assert ed["Platform"]["pitch"].dims[0] != ed["Platform"]["longitude"].dims[0]
assert ed["Platform"]["longitude"].dims[0] not in platform_preexisting_dims
assert ed["Platform"]["pitch"].dims[0] not in platform_preexisting_dims
# scalar variable
assert len(ed["Platform"]["water_level"].dims) == 0
@pytest.mark.parametrize(
["variable_mappings"],
[
pytest.param(
# lat and lon both exist, but aligned on different time dimension: should fail
{"longitude": "lon", "latitude": "lat"},
marks=pytest.mark.xfail(strict=True, reason="Fail since lat and lon not on the same time dimension")
),
pytest.param(
# only lon exists: should fail
{"longitude": "lon"},
marks=pytest.mark.xfail(strict=True, reason="Fail since only lon exists without lat")
),
],
ids=[
"lat_lon_diff_time",
"lon_only"
]
)
def test_update_platform_latlon(test_path, variable_mappings):
raw_file = test_path["EK60"] / "ooi" / "CE02SHBP-MJ01C-07-ZPLSCB101_OOI-D20191201-T000000.raw"
ed = echopype.open_raw(raw_file, sonar_model="EK60")
if "latitude" in variable_mappings:
extra_platform_data = xr.Dataset(
{
"lon": (["time1"], np.array([-100.0])),
"lat": (["time2"], np.array([-50.0])),
},
coords={
"time1": (["time1"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()])),
"time2": (["time2"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()]) + np.timedelta64(5, "s")),
},
)
else:
extra_platform_data = xr.Dataset(
{
"lon": (["time"], np.array([-100.0])),
},
coords={
"time": (["time"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()])),
},
)
ed.update_platform(extra_platform_data, variable_mappings=variable_mappings)
@pytest.mark.filterwarnings("ignore:No variables will be updated")
def test_update_platform_no_update(test_path):
raw_file = test_path["EK60"] / "ooi" / "CE02SHBP-MJ01C-07-ZPLSCB101_OOI-D20191201-T000000.raw"
ed = echopype.open_raw(raw_file, sonar_model="EK60")
extra_platform_data = xr.Dataset(
{
"lon": (["time"], np.array([-100.0])),
"lat": (["time"], np.array([-50.0])),
},
coords={
"time": (["time"], np.array([ed['Sonar/Beam_group1'].ping_time.values.min()])),
},
)
# variable names in mappings different from actual external dataset
variable_mappings = {"longitude": "longitude", "latitude": "latitude"}
ed.update_platform(extra_platform_data, variable_mappings=variable_mappings)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,835 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/qc/api.py | from typing import List, Optional
import numpy as np
import xarray as xr
from ..echodata import EchoData
from ..utils.log import _init_logger
logger = _init_logger(__name__)
def _clean_reversed(time_old: np.ndarray, win_len: int):
time_old_diff = np.diff(time_old)
# get indices of arr_diff with negative values
neg_idx = np.argwhere(time_old_diff < np.timedelta64(0, "ns")).flatten()
# substitute out the reversed timestamp using the previous one
new_diff = []
for ni in neg_idx:
local_win_idx = ni + np.arange(-win_len, 0)
if local_win_idx[0] < 0:
first_valid_idx = np.argwhere(local_win_idx == 0).flatten()[0]
local_win_idx = local_win_idx[first_valid_idx:]
new_diff.append(np.median(time_old_diff[local_win_idx]))
time_old_diff[neg_idx] = new_diff
# perform cumulative sum of differences after 1st neg index
c_diff = np.cumsum(time_old_diff[neg_idx[0] :], axis=0)
# create new array that preserves differences, but enforces increasing vals
new_time = time_old.copy()
new_time[neg_idx[0] + 1 :] = new_time[neg_idx[0]] + c_diff
return new_time
def coerce_increasing_time(
ds: xr.Dataset, time_name: str = "ping_time", win_len: int = 100
) -> None:
"""
Coerce a time coordinate so that it always flows forward. If coercion
is necessary, the input `ds` will be directly modified.
Parameters
----------
ds : xr.Dataset
a dataset for which the time coordinate needs to be corrected
time_name : str
name of the time coordinate to be corrected
win_len : int
length of the local window before the reversed timestamp within which
the median pinging interval is used to infer the next ping time
Returns
-------
the input dataset but with specified time coordinate coerced to flow forward
Notes
-----
This is to correct for problems sometimes observed in EK60/80 data
where a time coordinate (``ping_time`` or ``time1``) would suddenly
go backward for one ping, but then the rest of the pinging interval
would remain undisturbed.
"""
ds[time_name].data[:] = _clean_reversed(ds[time_name].data, win_len)
def exist_reversed_time(ds, time_name):
"""Test for occurrence of time reversal in specified datetime coordinate variable.
Parameters
----------
ds : xr.Dataset
a dataset for which the time coordinate will be tested
time_name : str
name of the time coordinate to be tested
Returns
-------
`True` if at least one time reversal is found, `False` otherwise.
"""
return (np.diff(ds[time_name]) < np.timedelta64(0, "ns")).any()
def check_and_correct_reversed_time(
combined_group: xr.Dataset, time_str: str, ed_group: str
) -> Optional[xr.DataArray]:
"""
Makes sure that the time coordinate ``time_str`` in
``combined_group`` is in the correct order and corrects
it, if it is not. If coercion is necessary, the input
`combined_group` will be directly modified.
Parameters
----------
combined_group : xr.Dataset
Dataset representing a combined EchoData group
time_str : str
Name of time coordinate to be checked and corrected
ed_group : str
Name of ``EchoData`` group name
Returns
-------
old_time : xr.DataArray or None
If correction is necessary, returns the time before
reversal correction, otherwise returns None
Warns
-----
UserWarning
If a time reversal is detected
"""
if time_str in combined_group and exist_reversed_time(combined_group, time_str):
logger.warning(
f"{ed_group} {time_str} reversal detected; {time_str} will be corrected" # noqa
" (see https://github.com/OSOceanAcoustics/echopype/pull/297)"
)
old_time = combined_group[time_str].copy()
coerce_increasing_time(combined_group, time_name=time_str)
else:
old_time = None
return old_time
def create_old_time_array(group: str, old_time_in: xr.DataArray) -> xr.DataArray:
"""
Creates an old time array with the appropriate values, name,
attributes, and encoding.
Parameters
----------
group: str
The name of the ``EchoData`` group that contained
the old time
old_time_in: xr.DataArray
The uncorrected old time
Returns
-------
old_time_array: xr.DataArray
The newly created old time array
"""
# make a copy, so we don't change the source array
old_time = old_time_in.copy()
# get name of old time and dim for Provenance group
ed_name = group.replace("-", "_").replace("/", "_").lower()
old_time_name = ed_name + "_old_" + old_time.name
old_time_name_dim = old_time_name + "_dim"
# construct old time attributes
attributes = old_time.attrs
attributes["comment"] = f"Uncorrected {old_time.name} from the combined group {group}."
# create old time array
old_time_array = xr.DataArray(
data=old_time.values, dims=[old_time_name_dim], attrs=attributes, name=old_time_name
)
# set encodings
old_time_array.encoding = old_time.encoding
return old_time_array
def orchestrate_reverse_time_check(
ed_comb: EchoData,
zarr_store: str,
possible_time_dims: List[str],
storage_options: dict,
consolidated: bool = True,
) -> None:
"""
Performs a reverse time check of all groups and
each time dimension within the group. If a reversed
time is found it will be corrected in ``ed_comb``,
updated in the zarr store, the old time will be
added to the ``Provenance`` group in ``ed_comb``,
the old time will be written to the zarr store,
and the attribute ``reversed_ping_times`` in the
``Provenance`` group will be set to ``1``.
Parameters
----------
ed_comb: EchoData
``EchoData`` object that has been constructed from
combined ``EchoData`` objects
zarr_store: str
The zarr store containing the ``ed_comb`` data
possible_time_dims: list of str
All possible time dimensions that can occur within
``ed_comb``, which should be checked
storage_options: dict
Additional keywords to pass to the filesystem class.
consolidated : bool
Flag to consolidate zarr metadata.
Defaults to ``True``
Notes
-----
If correction is necessary, ``ed_comb`` will be
directly modified.
"""
# set Provenance attribute to zero in ed_comb
ed_comb["Provenance"].attrs["reversed_ping_times"] = 0
# set Provenance attribute to zero in zarr (Dataset needed for metadata creation)
only_attrs_ds = xr.Dataset(attrs=ed_comb["Provenance"].attrs)
only_attrs_ds.to_zarr(
zarr_store,
group="Provenance",
mode="a",
storage_options=storage_options,
consolidated=consolidated,
)
for group in ed_comb.group_paths:
if group != "Platform/NMEA":
# Platform/NMEA is skipped because we found that the times which correspond to
# other non-GPS messages are often out of order and correcting them is not
# possible with the current implementation of _clean_ping_time in qc.api due
# to excessive recursion. There is also no obvious advantage in correcting
# the order of these timestamps.
# get all time dimensions of the group
ed_comb_time_dims = set(ed_comb[group].dims).intersection(possible_time_dims)
for time in ed_comb_time_dims:
old_time = check_and_correct_reversed_time(
combined_group=ed_comb[group], time_str=time, ed_group=group
)
if old_time is not None:
old_time_array = create_old_time_array(group, old_time)
# put old times in Provenance and modify attribute
ed_comb["Provenance"][old_time_array.name] = old_time_array
ed_comb["Provenance"].attrs["reversed_ping_times"] = 1
# save old time to zarr store
old_time_ds = old_time_array.to_dataset()
old_time_ds.attrs = ed_comb["Provenance"].attrs
old_time_ds.to_zarr(
zarr_store,
group="Provenance",
mode="a",
storage_options=storage_options,
consolidated=consolidated,
)
# save corrected time to zarr store
ed_comb[group][[time]].to_zarr(
zarr_store,
group=group,
mode="r+",
storage_options=storage_options,
consolidated=consolidated,
)
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"/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", 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"/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,836 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/visualize/__init__.py | """
Visualization module to quickly plot raw, Sv, and MVBS dataset.
**NOTE: To use this subpackage. `Matplotlib` and `cmocean` package must be installed.**
"""
from .api import create_echogram
from . import cm
__all__ = ["create_echogram", "cm"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,837 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/qc/test_qc.py | import numpy as np
import xarray as xr
from echopype.qc import coerce_increasing_time, exist_reversed_time
from echopype.qc.api import _clean_reversed
import pytest
@pytest.fixture
def ds_time():
return xr.Dataset(
data_vars={"a": ("time", np.arange(36))},
coords={
"time": np.array([
'2021-07-15T22:59:54.328000000', '2021-07-15T22:59:54.598000128',
'2021-07-15T22:59:54.824999936', '2021-07-15T22:59:55.170999808',
'2021-07-15T22:59:56.172999680', '2021-07-15T22:59:55.467999744',
'2021-07-15T22:59:55.737999872', '2021-07-15T22:59:55.966000128',
'2021-07-15T22:59:56.467999744', '2021-07-15T22:59:56.813000192',
'2021-07-15T22:59:57.040999936', '2021-07-15T22:59:57.178999808',
'2021-07-15T22:59:58.178999808', '2021-07-15T22:59:57.821000192',
'2021-07-15T22:59:58.092000256', '2021-07-15T22:59:58.318999552',
'2021-07-15T22:59:58.730999808', '2021-07-15T22:59:59.092000256',
'2021-07-15T22:59:59.170999808', '2021-07-15T23:00:00.170999808',
'2021-07-15T23:00:01.170999808', '2021-07-15T22:59:59.719000064',
'2021-07-15T22:59:59.989999616', '2021-07-15T23:00:00.573000192',
'2021-07-15T23:00:00.843999744', '2021-07-15T23:00:01.071000064',
'2021-07-15T23:00:02.170999808', '2021-07-15T23:00:03.181000192',
'2021-07-15T23:00:01.692999680', '2021-07-15T23:00:02.054000128',
'2021-07-15T23:00:02.592999936', '2021-07-15T23:00:02.864000000',
'2021-07-15T23:00:03.480999936', '2021-07-15T23:00:04.171999744',
'2021-07-15T23:00:05.179999744', '2021-07-15T23:00:03.771999744'],
dtype='datetime64[ns]')
},
)
@pytest.mark.parametrize(
["win_len", "input_arr", "expected_arr"],
[
(
2,
np.array([0,1,2,3,4,2,3,4,6,8,10,11,12,13,15,17,19,13,15,17,21], dtype="datetime64[ns]"),
np.array([0,1,2,3,4,5,6,7,9,11,13,14,15,16,18,20,22,24,26,28,32], dtype="datetime64[ns]")
),
(
6,
(np.array([0,1,2,3,4,2,3,4,6,8,10,11,12,13,15,17,19,13,15,17,21])*2).astype("datetime64[ns]"),
np.array([0,2,4,6,8,10,12,14,18,22,26,28,30,32,36,40,44,47,51,55,63]).astype("datetime64[ns]"),
),
],
ids=[
"win_len2",
"win_len6"
]
)
def test__clean_reversed(win_len, input_arr, expected_arr):
arr_fixed = _clean_reversed(input_arr, win_len)
# fixed array follows monotonically increasing order
arr_fixed_diff = np.diff(arr_fixed)
assert np.argwhere(arr_fixed_diff < np.timedelta64(0, "ns")).flatten().size == 0
# new filled value should have diff being the median of local_win_len before reversal
assert np.all(arr_fixed == expected_arr)
def test_coerce_increasing_time(ds_time):
# fixed timestamp follows monotonically increasing order
coerce_increasing_time(ds_time, "time")
assert np.argwhere(ds_time["time"].diff(dim="time").data < np.timedelta64(0, "ns")).flatten().size == 0
def test_exist_reversed_time(ds_time):
# data has reversed timestamps to begin with
assert exist_reversed_time(ds_time, "time") == True
# after correction there are no reversed timestamps
coerce_increasing_time(ds_time, "time")
assert exist_reversed_time(ds_time, "time") == False
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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,838 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/core.py | import os
import re
from typing import TYPE_CHECKING, Any, Callable, Dict, Union
from fsspec.mapping import FSMap
from typing_extensions import Literal
from .convert.parse_ad2cp import ParseAd2cp
from .convert.parse_azfp import ParseAZFP
from .convert.parse_ek60 import ParseEK60
from .convert.parse_ek80 import ParseEK80
from .convert.parsed_to_zarr_ek60 import Parsed2ZarrEK60
from .convert.parsed_to_zarr_ek80 import Parsed2ZarrEK80
from .convert.set_groups_ad2cp import SetGroupsAd2cp
from .convert.set_groups_azfp import SetGroupsAZFP
from .convert.set_groups_ek60 import SetGroupsEK60
from .convert.set_groups_ek80 import SetGroupsEK80
if TYPE_CHECKING:
# Please keep SonarModelsHint updated with the keys of the SONAR_MODELS dict
SonarModelsHint = Literal["AZFP", "EK60", "ES70", "EK80", "ES80", "EA640", "AD2CP"]
PathHint = Union[str, os.PathLike, FSMap]
FileFormatHint = Literal[".nc", ".zarr"]
EngineHint = Literal["netcdf4", "zarr"]
def validate_azfp_ext(test_ext: str):
if not re.fullmatch(r"\.\d{2}[a-zA-Z]", test_ext):
raise ValueError(
'Expecting a file in the form ".XXY" '
f"where XX is a number and Y is a letter but got {test_ext}"
)
def validate_ext(ext: str) -> Callable[[str], None]:
def inner(test_ext: str):
if ext.casefold() != test_ext.casefold():
raise ValueError(f"Expecting a {ext} file but got {test_ext}")
return inner
SONAR_MODELS: Dict["SonarModelsHint", Dict[str, Any]] = {
"AZFP": {
"validate_ext": validate_azfp_ext,
"xml": True,
"parser": ParseAZFP,
"parsed2zarr": None,
"set_groups": SetGroupsAZFP,
"dgram_zarr_vars": {},
},
"EK60": {
"validate_ext": validate_ext(".raw"),
"xml": False,
"parser": ParseEK60,
"parsed2zarr": Parsed2ZarrEK60,
"set_groups": SetGroupsEK60,
"dgram_zarr_vars": {"power": ["timestamp", "channel"], "angle": ["timestamp", "channel"]},
},
"ES70": {
"validate_ext": validate_ext(".raw"),
"xml": False,
"parser": ParseEK60,
"parsed2zarr": Parsed2ZarrEK60,
"set_groups": SetGroupsEK60,
"dgram_zarr_vars": {"power": ["timestamp", "channel"], "angle": ["timestamp", "channel"]},
},
"EK80": {
"validate_ext": validate_ext(".raw"),
"xml": False,
"parser": ParseEK80,
"parsed2zarr": Parsed2ZarrEK80,
"set_groups": SetGroupsEK80,
"dgram_zarr_vars": {
"power": ["timestamp", "channel_id"],
"complex": ["timestamp", "channel_id"],
"angle": ["timestamp", "channel_id"],
},
},
"ES80": {
"validate_ext": validate_ext(".raw"),
"xml": False,
"parser": ParseEK80,
"parsed2zarr": Parsed2ZarrEK80,
"set_groups": SetGroupsEK80,
"dgram_zarr_vars": {
"power": ["timestamp", "channel_id"],
"complex": ["timestamp", "channel_id"],
"angle": ["timestamp", "channel_id"],
},
},
"EA640": {
"validate_ext": validate_ext(".raw"),
"xml": False,
"parser": ParseEK80,
"parsed2zarr": Parsed2ZarrEK80,
"set_groups": SetGroupsEK80,
"dgram_zarr_vars": {
"power": ["timestamp", "channel_id"],
"complex": ["timestamp", "channel_id"],
"angle": ["timestamp", "channel_id"],
},
},
"AD2CP": {
"validate_ext": validate_ext(".ad2cp"),
"xml": False,
"parser": ParseAd2cp,
"parsed2zarr": None,
"set_groups": SetGroupsAd2cp,
"dgram_zarr_vars": {},
},
}
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,839 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parse_ek60.py | from .parse_base import ParseEK
class ParseEK60(ParseEK):
"""Class for converting data from Simrad EK60 echosounders."""
def __init__(self, file, params, storage_options={}, dgram_zarr_vars={}):
super().__init__(file, params, storage_options, dgram_zarr_vars)
def _select_datagrams(self, params):
# Translates user input into specific datagrams or ALL
if params == "ALL":
return ["ALL"]
elif params == "GPS":
return ["NME"]
else:
raise ValueError("Unknown data type", params)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,840 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_cal_params.py | import pytest
import numpy as np
import xarray as xr
from echopype.calibrate.cal_params import (
CAL_PARAMS, param2da, sanitize_user_cal_dict, _get_interp_da,
get_cal_params_AZFP, get_cal_params_EK, get_vend_cal_params_power
)
@pytest.fixture
def freq_center():
return xr.DataArray(
[[25, 55]],
dims=["ping_time", "channel"],
coords={"channel": ["chA", "chB"], "ping_time": [1]}
)
@pytest.fixture
def vend_AZFP():
"""
A mock AZFP Vendor_specific group for cal testing.
"""
da = xr.DataArray([10, 20], dims=["channel"], coords={"channel": ["chA", "chB"]})
vend = xr.Dataset()
for p_name in CAL_PARAMS["AZFP"]:
if p_name != "equivalent_beam_angle":
da.name = p_name
vend[p_name] = da
return vend
@pytest.fixture
def beam_AZFP():
"""
A mock AZFP Sonar/Beam_group1 group for cal testing.
"""
beam = xr.Dataset()
beam["equivalent_beam_angle"] = xr.DataArray(
[[10, 20]],
dims=["ping_time", "channel"],
coords={"channel": ["chA", "chB"], "ping_time": [1]},
)
return beam.transpose("channel", "ping_time")
@pytest.fixture
def vend_EK():
"""
A mock EK Sonar/Beam_groupX group for cal testing.
"""
vend = xr.Dataset()
for p_name in ["sa_correction", "gain_correction"]:
vend[p_name] = xr.DataArray(
np.array([[10, 20, 30, 40], [110, 120, 130, 140]]),
dims=["channel", "pulse_length_bin"],
coords={"channel": ["chA", "chB"], "pulse_length_bin": [0, 1, 2, 3]},
)
vend["pulse_length"] = xr.DataArray(
np.array([[64, 128, 256, 512], [128, 256, 512, 1024]]),
coords={"channel": vend["channel"], "pulse_length_bin": vend["pulse_length_bin"]}
)
vend["impedance_transceiver"] = xr.DataArray(
[1000, 2000], coords={"channel": vend["channel"]}
)
vend["transceiver_type"] = xr.DataArray(
["WBT", "WBT"], coords={"channel": vend["channel"]}
)
return vend
@pytest.fixture
def beam_EK():
"""
A mock EK Sonar/Beam_groupX group for cal testing.
"""
beam = xr.Dataset()
for p_name in [
"equivalent_beam_angle",
"angle_offset_alongship", "angle_offset_athwartship",
"angle_sensitivity_alongship", "angle_sensitivity_athwartship",
"beamwidth_twoway_alongship", "beamwidth_twoway_athwartship"
]:
beam[p_name] = xr.DataArray(
np.array([[123], [456]]),
dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]},
)
beam["frequency_nominal"] = xr.DataArray([25, 55], dims=["channel"], coords={"channel": ["chA", "chB"]})
return beam.transpose("channel", "ping_time")
@pytest.mark.parametrize(
("p_val", "channel", "da_output"),
[
# input p_val a scalar, input channel a list
(1, ["chA", "chB"], xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "chB"]})),
# input p_val a list, input channel an xr.DataArray
(
[1, 2],
xr.DataArray(["chA", "chB"], dims=["channel"], coords={"channel": ["chA", "chB"]}),
xr.DataArray([1, 2], dims=["channel"], coords={"channel": ["chA", "chB"]})
),
# input p_val a list with the wrong length: this should fail
pytest.param(
[1, 2, 3], ["chA", "chB"], None,
marks=pytest.mark.xfail(strict=True, reason="Fail since lengths of p_val and channel are not identical")
),
],
ids=[
"in_p_val_scalar_channel_list",
"in_p_val_list_channel_xrda",
"in_p_val_list_wrong_length",
]
)
def test_param2da(p_val, channel, da_output):
da_assembled = param2da(p_val, channel)
assert da_assembled.identical(da_output)
@pytest.mark.parametrize(
("sonar_type", "user_dict", "channel", "out_dict"),
[
# sonar_type only allows EK or AZFP
pytest.param(
"XYZ", None, None, None,
marks=pytest.mark.xfail(strict=True, reason="Fail since sonar_type is not 'EK' nor 'AZFP'")
),
# input channel
# - is not a list nor an xr.DataArray: fail with value error
pytest.param(
"EK80", 1, None, None,
marks=pytest.mark.xfail(strict=True, reason="Fail since channel has to be either a list or an xr.DataArray"),
),
# TODO: input channel has different order than those in the inarg channel
# input param dict
# - contains extra param: should come out with only those defined in CAL_PARAMS
# - contains missing param: missing ones (wrt CAL_PARAMS) should be empty
pytest.param("EK80", {"extra_param": 1}, ["chA", "chB"], dict.fromkeys(CAL_PARAMS["EK80"])),
# input param:
# - is xr.DataArray without channel coorindate: fail with value error
pytest.param(
"EK80",
{"sa_correction": xr.DataArray([1, 1], dims=["some_coords"], coords={"some_coords": ["A", "B"]})},
["chA", "chB"], None,
marks=pytest.mark.xfail(strict=True, reason="input sa_correction does not contain a 'channel' coordinate"),
),
# input individual param:
# - with channel cooridinate but not identical to argin channel: fail with value error
pytest.param(
"EK80",
{"sa_correction": xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "B"]})},
["chA", "chB"], None,
marks=pytest.mark.xfail(strict=True,
reason="input sa_correction contains a 'channel' coordinate but it is not identical with input channel"),
),
# input individual param:
# - with channel cooridinate identical to argin channel: should pass
pytest.param(
"EK80",
{"sa_correction": xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "chB"]})},
["chA", "chB"],
dict(dict.fromkeys(CAL_PARAMS["EK80"]),
**{"sa_correction": xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "chB"]})}),
),
# input individual param:
# - a scalar needing to be organized to xr.DataArray at output via param2da: should pass
pytest.param(
"EK80",
{"sa_correction": 1},
["chA", "chB"],
dict(dict.fromkeys(CAL_PARAMS["EK80"]),
**{"sa_correction": xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "chB"]})}),
),
# input individual param:
# - a list needing to be organized to xr.DataArray at output via param2da: should pass
pytest.param(
"EK80",
{"sa_correction": [1, 2]},
["chA", "chB"],
dict(dict.fromkeys(CAL_PARAMS["EK80"]),
**{"sa_correction": xr.DataArray([1, 2], dims=["channel"], coords={"channel": ["chA", "chB"]})}),
),
# input individual param:
# - a list with wrong length (ie not identical to channel): fail with value error
pytest.param(
"EK80", {"sa_correction": [1, 2, 3]}, ["chA", "chB"], None,
marks=pytest.mark.xfail(strict=True,
reason="input sa_correction contains a list of wrong length that does not match that of channel"),
),
],
ids=[
"sonar_type_invalid",
"channel_invalid",
"in_extra_param",
"in_da_no_channel_coord",
"in_da_channel_not_identical",
"in_da_channel_identical",
"in_scalar",
"in_list",
"in_list_wrong_length",
],
)
def test_sanitize_user_cal_dict(sonar_type, user_dict, channel, out_dict):
sanitized_dict = sanitize_user_cal_dict(sonar_type, user_dict, channel)
assert isinstance(sanitized_dict, dict)
assert len(sanitized_dict) == len(out_dict)
for p_name, p_val in sanitized_dict.items():
if isinstance(p_val, xr.DataArray):
assert p_val.identical(out_dict[p_name])
else:
assert p_val == out_dict[p_name]
@pytest.mark.parametrize(
("da_param", "alternative", "da_output"),
[
# da_param: alternative is const: output is xr.DataArray with all const
(
None,
1,
xr.DataArray([[1], [1]], dims=["channel", "ping_time"], coords={"channel": ["chA", "chB"], "ping_time": [1]})
),
# da_param: alternative is xr.DataArray: output selected with the right channel
(
None,
xr.DataArray([1, 1, 2], dims=["channel"], coords={"channel": ["chA", "chB", "chC"]}),
xr.DataArray([[1], [1]], dims=["channel", "ping_time"], coords={"channel": ["chA", "chB"], "ping_time": [1]})
),
# da_param: xr.DataArray with freq-dependent values/coordinates
# - output should be interpolated with the right values
(
xr.DataArray(
np.array([[1, 2, 3, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, 4, 5, 6],
[np.nan, 2, 3, 4, np.nan, np.nan]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chA", "chB", "chC"],
"cal_frequency": [10, 20, 30, 40, 50, 60]},
),
None,
xr.DataArray([[2.5], [5.5]], dims=["channel", "ping_time"], coords={"ping_time": [1], "channel": ["chA", "chB"]}),
),
# da_param: xr.DataArray with only one channel having freq-dependent values/coordinates
# - that single channel should be interpolated with the right value
# - other channels will use alternative
# - alternative could be of the following form:
# - scalar
(
xr.DataArray(
np.array([[np.nan, np.nan, np.nan, 4, 5, 6]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chB"],
"cal_frequency": [10, 20, 30, 40, 50, 60]},
),
75,
xr.DataArray(
[[75], [5.5]],
dims=["channel", "ping_time"],
coords={"ping_time": [1], "channel": ["chA", "chB"]}
),
),
# - xr.DataArray with coordinates channel, ping_time
(
xr.DataArray(
np.array([[np.nan, np.nan, np.nan, 4, 5, 6]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chB"],
"cal_frequency": [10, 20, 30, 40, 50, 60]},
),
xr.DataArray(
np.array([[100], [200]]),
dims=["channel", "ping_time"],
coords={"ping_time": [1], "channel": ["chA", "chB"]},
),
xr.DataArray(
[[100], [5.5]],
dims=["channel", "ping_time"],
coords={"ping_time": [1], "channel": ["chA", "chB"]}
),
# TODO: cases where freq_center does not have the ping_time dimension
# this is the case for CW data since freq_center = beam["frequency_nominal"]
# this was caught by the file in test_compute_Sv_ek80_CW_complex()
# TODO: cases where freq_center contains only a single frequency
# in this case had to use freq_center.sel(channel=ch_id).size because
# len(freq_center.sel(channel=ch_id)) is an invalid statement
# this was caught by the file in test_compute_Sv_ek80_CW_power_BB_complex()
),
],
ids=[
"in_None_alt_const",
"in_None_alt_da",
"in_da_all_channel_out_interp",
"in_da_some_channel_alt_scalar",
"in_da_some_channel_alt_da2coords", # channel, ping_time
]
)
def test_get_interp_da(freq_center, da_param, alternative, da_output):
da_interp = _get_interp_da(da_param, freq_center, alternative)
assert da_interp.identical(da_output)
@pytest.mark.parametrize(
("user_dict", "out_dict"),
[
# input param is a scalar
(
{"EL": 1, "equivalent_beam_angle": 2},
dict(
{p_name: xr.DataArray([10, 20], dims=["channel"], coords={"channel": ["chA", "chB"]}) for p_name in CAL_PARAMS["AZFP"]},
**{
"EL": xr.DataArray([1, 1], dims=["channel"], coords={"channel": ["chA", "chB"]}),
"equivalent_beam_angle": xr.DataArray([2, 2], dims=["channel"], coords={"channel": ["chA", "chB"]}),
}
),
),
# input param is a list
(
{"EL": [1, 2], "equivalent_beam_angle": [3, 4]},
dict(
{p_name: xr.DataArray([10, 20], dims=["channel"], coords={"channel": ["chA", "chB"]}) for p_name in CAL_PARAMS["AZFP"]},
**{
"EL": xr.DataArray([1, 2], dims=["channel"], coords={"channel": ["chA", "chB"]}),
"equivalent_beam_angle": xr.DataArray([3, 4], dims=["channel"], coords={"channel": ["chA", "chB"]}),
}
),
),
# input param is a list of wrong length: this should fail
pytest.param(
{"EL": [1, 2, 3], "equivalent_beam_angle": [3, 4]}, None,
marks=pytest.mark.xfail(strict=True, reason="Fail since lengths of input list and channel are not identical"),
),
# input param is an xr.DataArray with coordinate 'channel'
(
{
"EL": xr.DataArray([1, 2], dims=["channel"], coords={"channel": ["chA", "chB"]}),
"equivalent_beam_angle": xr.DataArray([3, 4], dims=["channel"], coords={"channel": ["chA", "chB"]}),
},
dict(
{p_name: xr.DataArray([10, 20], dims=["channel"], coords={"channel": ["chA", "chB"]}) for p_name in CAL_PARAMS["AZFP"]},
**{
"EL": xr.DataArray([1, 2], dims=["channel"], coords={"channel": ["chA", "chB"]}),
"equivalent_beam_angle": xr.DataArray([3, 4], dims=["channel"], coords={"channel": ["chA", "chB"]}),
}
),
),
# input param is an xr.DataArray with coordinate 'channel' but wrong length: this should fail
pytest.param(
{
"EL": xr.DataArray([1, 2, 3], dims=["channel"], coords={"channel": ["chA", "chB", "chC"]}),
"equivalent_beam_angle": xr.DataArray([3, 4, 5], dims=["channel"], coords={"channel": ["chA", "chB", "chC"]}),
}, None,
marks=pytest.mark.xfail(strict=True, reason="Fail since lengths of input data array channel and data channel are not identical"),
),
],
ids=[
"in_scalar",
"in_list",
"in_list_wrong_length",
"in_da",
"in_da_wrong_length",
]
)
def test_get_cal_params_AZFP(beam_AZFP, vend_AZFP, user_dict, out_dict):
cal_dict = get_cal_params_AZFP(beam=beam_AZFP, vend=vend_AZFP, user_dict=user_dict)
for p_name, p_val in cal_dict.items():
# remove name for all da
p_val.name = None
out_val = out_dict[p_name]
out_val.name = None
assert p_val.identical(out_val)
# The test above 'test_get_cal_params_AZFP' covers the cases where user input param
# is one of the following: a scalar, list, and xr.DataArray of coords/dims ('channel')
# Here we only test for the following new cases:
# - where all params are input by user
# - input xr.DataArray has coords/dims (cal_channel_id, cal_frequency)
@pytest.mark.parametrize(
("user_dict", "out_dict", "freq_center_scaling"),
[
# input xr.DataArray has coords/dims (cal_channel_id, cal_frequency)
# no freq-related scaling: freq_center = beam_EK["frequency_nominal"]
(
{
"gain_correction": xr.DataArray(
np.array([[1, 2, 3, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, 4, 5, 6]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chA", "chB"],
"cal_frequency": [10, 20, 30, 40, 50, 60]},
),
# add sa_correction here to bypass things going into get_vend_cal_params_power
"sa_correction": xr.DataArray(
np.array([111, 222]), dims=["channel"], coords={"channel": ["chA", "chB"]},
),
},
dict(
{
p_name: xr.DataArray(
[[123], [456]],
dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]},
)
for p_name in CAL_PARAMS["EK80"]
},
**{
"gain_correction": xr.DataArray(
[[2.5], [5.5]],
dims=["channel", "ping_time"],
coords={"ping_time": [1], "channel": ["chA", "chB"]},
),
"sa_correction": xr.DataArray(
np.array([111, 222]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
"impedance_transducer": xr.DataArray(
np.array([[75], [75]]), dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]}
),
"impedance_transceiver": xr.DataArray(
np.array([1000, 2000]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
"receiver_sampling_frequency": xr.DataArray(
np.array([1500000, 1500000]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
},
),
1, # no scaling of freq_center
),
# input xr.DataArray has coords/dims (cal_channel_id, cal_frequency)
# with freq-related scaling: freq_center != beam_EK["frequency_nominal"]
(
{
"gain_correction": xr.DataArray(
np.array([[1, 2, 3, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, 4, 5, 6]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chA", "chB"],
"cal_frequency": [10, 20, 30, 40, 50, 60]},
),
# add sa_correction here to bypass things going into get_vend_cal_params_power
"sa_correction": xr.DataArray(
np.array([111, 222]), dims=["channel"], coords={"channel": ["chA", "chB"]},
),
},
dict(
{
p_name: xr.DataArray(
[[123], [456]],
dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]},
)
for p_name in CAL_PARAMS["EK80"]
},
**{
"gain_correction": xr.DataArray(
np.array([[2.5], [5.5]]) * 0.79, # scaled by the factor as freq_center in function body
dims=["channel", "ping_time"],
coords={"ping_time": [1], "channel": ["chA", "chB"]},
),
"sa_correction": xr.DataArray(
np.array([111, 222]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
"impedance_transducer": xr.DataArray(
np.array([[75], [75]]), dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]}
),
"impedance_transceiver": xr.DataArray(
np.array([1000, 2000]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
"receiver_sampling_frequency": xr.DataArray(
np.array([1500000, 1500000]), dims=["channel"],
coords={"channel": ["chA", "chB"]}
),
},
),
0.79, # with scaling of freq_center
),
pytest.param(
{
"gain_correction": xr.DataArray(
np.array([[1, 2, 3, np.nan], [np.nan, 4, 5, 6], [np.nan, 2, 3, np.nan]]),
dims=["cal_channel_id", "cal_frequency"],
coords={"cal_channel_id": ["chA", "chB", "chC"],
"cal_frequency": [10, 20, 30, 40]},
),
},
None,
1,
marks=pytest.mark.xfail(strict=True, reason="Fail since cal_channel_id in input param does not match channel of data"),
),
],
ids=[
"in_da_freq_dep_no_scaling",
"in_da_freq_dep_with_scaling",
"in_da_freq_dep_channel_mismatch",
]
)
def test_get_cal_params_EK80_BB(beam_EK, vend_EK, freq_center, user_dict, out_dict, freq_center_scaling):
# If freq_center != beam_EK["frequency_nominal"], the following params will be scaled:
# - angle_sensitivity_alongship/athwartship (by fc/fn)
# - beamwidth_alongship/athwartship (by fn/fc)
# - equivalent_beam_angle (by (fn/fc)^2)
freq_center = freq_center * freq_center_scaling # scale by an arbitrary number
for p in ["angle_sensitivity_alongship", "angle_sensitivity_athwartship"]:
out_dict[p] = out_dict[p] * freq_center / beam_EK["frequency_nominal"]
for p in ["beamwidth_alongship", "beamwidth_athwartship"]:
out_dict[p] = out_dict[p] * beam_EK["frequency_nominal"] / freq_center
out_dict["equivalent_beam_angle"] = (
out_dict["equivalent_beam_angle"] + 20 * np.log10(beam_EK["frequency_nominal"] / freq_center)
)
cal_dict = get_cal_params_EK(
waveform_mode="BB", freq_center=freq_center, beam=beam_EK, vend=vend_EK, user_dict=user_dict
)
for p_name, p_val in cal_dict.items():
print(p_name)
# remove name for all da
p_val.name = None
out_val = out_dict[p_name]
out_val.name = None
assert p_val.identical(out_dict[p_name])
@pytest.mark.parametrize(
("user_dict", "out_dict"),
[
# cal_params should not contain:
# impedance_transducer, impedance_transceiver, receiver_sampling_frequency
(
{
# add sa_correction here to bypass things going into get_vend_cal_params_power
"gain_correction": xr.DataArray(
[555, 777], dims=["channel"], coords={"channel": ["chA", "chB"]},
),
# add sa_correction here to bypass things going into get_vend_cal_params_power
"sa_correction": xr.DataArray(
[111, 222], dims=["channel"], coords={"channel": ["chA", "chB"]},
)
},
dict(
{
p_name: xr.DataArray(
[[123], [456]],
dims=["channel", "ping_time"],
coords={"channel": ["chA", "chB"], "ping_time": [1]},
)
for p_name in [
"sa_correction", "gain_correction", "equivalent_beam_angle",
"angle_offset_alongship", "angle_offset_athwartship",
"angle_sensitivity_alongship", "angle_sensitivity_athwartship",
"beamwidth_alongship", "beamwidth_athwartship",
]
},
**{
"gain_correction": xr.DataArray(
[555, 777], dims=["channel"], coords={"channel": ["chA", "chB"]},
),
"sa_correction": xr.DataArray(
[111, 222], dims=["channel"], coords={"channel": ["chA", "chB"]}
),
},
),
),
],
ids=[
"in_da",
]
)
def test_get_cal_params_EK60(beam_EK, vend_EK, freq_center, user_dict, out_dict):
# Remove some variables from Vendor group to mimic EK60 data
vend_EK = vend_EK.drop("impedance_transceiver").drop("transceiver_type")
cal_dict = get_cal_params_EK(
waveform_mode="CW", freq_center=freq_center,
beam=beam_EK, vend=vend_EK,
user_dict=user_dict, sonar_type="EK60"
)
for p_name, p_val in cal_dict.items():
# remove name for all da
p_val.name = None
out_val = out_dict[p_name]
out_val.name = None
assert p_val.identical(out_dict[p_name])
@pytest.mark.parametrize(
("param", "beam", "da_output"),
[
# no NaN entry in transmit_duration_nominal
(
"sa_correction",
xr.DataArray(
np.array([[64, 256, 128, 512], [512, 1024, 256, 128]]).T,
dims=["ping_time", "channel"],
coords={"ping_time": [1, 2, 3, 4], "channel": ["chA", "chB"]},
name="transmit_duration_nominal",
).to_dataset(),
xr.DataArray(
np.array([[10, 30, 20, 40], [130, 140, 120, 110]]).T,
dims=["ping_time", "channel"],
coords={"ping_time": [1, 2, 3, 4], "channel": ["chA", "chB"]},
name="sa_correction",
).astype(np.float64),
),
# with NaN entry in transmit_duration_nominal
(
"sa_correction",
xr.DataArray(
np.array([[64, np.nan, 128, 512], [512, 1024, 256, np.nan]]).T,
dims=["ping_time", "channel"],
coords={"ping_time": [1, 2, 3, 4], "channel": ["chA", "chB"]},
name="transmit_duration_nominal",
).to_dataset(),
xr.DataArray(
np.array([[10, np.nan, 20, 40], [130, 140, 120, np.nan]]).T,
dims=["ping_time", "channel"],
coords={"ping_time": [1, 2, 3, 4], "channel": ["chA", "chB"]},
name="sa_correction",
),
),
],
ids=[
"in_no_nan",
"in_with_nan",
]
)
def test_get_vend_cal_params_power(vend_EK, beam, param, da_output):
da_param = get_vend_cal_params_power(beam, vend_EK, param)
assert da_param.identical(da_output)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", 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"/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,841 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_ecs.py | import pytest
from datetime import datetime
import numpy as np
import xarray as xr
from echopype.calibrate.ecs import ECSParser, ecs_ev2ep, conform_channel_order
@pytest.fixture
def ecs_path(test_path):
return test_path['ECS']
CORRECT_PARSED_PARAMS = {
"fileset": {
"SoundSpeed": 1496.0,
"TvgRangeCorrection": "BySamples",
"TvgRangeCorrectionOffset": 2.0,
},
"sourcecal": {
"T1": {
"AbsorptionCoefficient": 0.002822,
"EK60SaCorrection": -0.7,
"Ek60TransducerGain": 22.95,
"Frequency": 18.00,
"MajorAxis3dbBeamAngle": 10.82,
"MajorAxisAngleOffset": 0.25,
"MajorAxisAngleSensitivity": 13.89,
"MinorAxis3dbBeamAngle": 10.9,
"MinorAxisAngleOffset": -0.18,
"MinorAxisAngleSensitivity": 13.89,
"SoundSpeed": 1480.6,
"TwoWayBeamAngle": -17.37,
},
"T2": {
"AbsorptionCoefficient": 0.009855,
"EK60SaCorrection": -0.52,
"Ek60TransducerGain": 26.07,
"Frequency": 38.00,
"MajorAxis3dbBeamAngle": 6.85,
"MajorAxisAngleOffset": 0.0,
"MajorAxisAngleSensitivity": 21.970001,
"MinorAxis3dbBeamAngle": 6.81,
"MinorAxisAngleOffset": -0.08,
"MinorAxisAngleSensitivity": 21.970001,
"SoundSpeed": 1480.6,
"TwoWayBeamAngle": -21.01,
},
"T3": {
"AbsorptionCoefficient": 0.032594,
"EK60SaCorrection": -0.3,
"Ek60TransducerGain": 26.55,
"Frequency": 120.00,
"MajorAxis3dbBeamAngle": 6.52,
"MajorAxisAngleOffset": 0.37,
"MajorAxisAngleSensitivity": 23.12,
"MinorAxis3dbBeamAngle": 6.58,
"MinorAxisAngleOffset": -0.05,
"MinorAxisAngleSensitivity": 23.12,
"SoundSpeed": 1480.6,
"TwoWayBeamAngle": -20.47,
},
},
"localcal": {"MyCal": {"TwoWayBeamAngle": -17.37}},
}
env_params_dict = {
"sound_speed": [1480.6, 1480.6, 1480.6],
"sound_absorption": [0.002822, 0.009855, 0.032594],
"frequency_nominal": [1.8e+04, 3.8e+04, 1.2e+05],
}
CORRECT_ENV_DATASET = xr.Dataset(
{k: (["channel"], v) for k, v in env_params_dict.items()}, coords={"channel": np.arange(3)}
)
cal_params_dict = {
"sa_correction": [-0.7, -0.52, -0.3],
"gain_correction": [22.95, 26.07, 26.55],
"frequency_nominal": [1.8e+04, 3.8e+04, 1.2e+05],
"beamwidth_athwartship": [10.82, 6.85, 6.52],
"angle_offset_athwartship": [0.25, 0.0, 0.37],
"angle_sensitivity_athwartship": [13.89, 21.970001, 23.12],
"beamwidth_alongship": [10.9, 6.81, 6.58],
"angle_offset_alongship": [-0.18, -0.08, -0.05],
"angle_sensitivity_alongship": [13.89, 21.970001, 23.12],
"equivalent_beam_angle": [-17.37, -17.37, -17.37],
}
CORRECT_CAL_DATASET = xr.Dataset(
{k: (["channel"], v) for k, v in cal_params_dict.items()}, coords={"channel": np.arange(3)}
)
def test_convert_ecs_ek60_hake(ecs_path):
# Test converting ECS from hake survey (not all variables are used, ie some has '#' in front)
ecs_path = ecs_path / "Summer2017_JuneCal_3freq_mod.ecs"
ecs = ECSParser(ecs_path)
ecs.parse()
# Spot test parsed outcome
assert ecs.data_type == "SimradEK60Raw"
assert ecs.version == "1.00"
assert ecs.file_creation_time == datetime(
year=2015, month=6, day=19, hour=23, minute=26, second=4
)
assert ecs.parsed_params == CORRECT_PARSED_PARAMS
# Test ECS hierarchy
dict_ev_params = ecs.get_cal_params()
# SourceCal overwrite FileSet settings
assert dict_ev_params["T1"]["SoundSpeed"] == 1480.60
# LocalCal overwrites SourceCal
assert dict_ev_params["T2"]["TwoWayBeamAngle"] == -17.37
# Test assembled datasets
ds_env, ds_cal, _ = ecs_ev2ep(dict_ev_params, "EK60")
assert ds_cal.identical(CORRECT_CAL_DATASET)
assert ds_env.identical(CORRECT_ENV_DATASET)
def test_convert_ecs_ek80_template(ecs_path):
# Test converting template ECS generated by Echoview, with all '#' removed (ie use all params)
ecs_path = ecs_path / "Simrad_EK80_ES80_WBAT_EKAuto_Kongsberg_EA640_nohash.ecs"
ecs = ECSParser(ecs_path)
ecs.parse()
dict_ev_params = ecs.get_cal_params() # applies ECS hierarchy
env_params, cal_params, cal_params_BB = ecs_ev2ep(dict_ev_params, "EK80")
assert dict_ev_params["T1"]["SoundSpeed"] == 1480.60
def test_check_source_channel_order():
ds_in = xr.Dataset(
{
"var1": (["channel"], [1, 2, 3]),
"frequency_nominal": (["channel"], [18000, 38000, 120000]),
},
coords={"channel": np.arange(3)}
)
freq_ref = xr.DataArray(
[38000, 18000, 120000],
coords={"channel": ["chB", "chA", "chC"]},
dims=["channel"],
)
ds_out = conform_channel_order(ds_in, freq_ref)
assert np.all(ds_out["channel"].values == ["chB", "chA", "chC"]) # channel follow those of freq_ref
assert not "frequency_nominal" in ds_out # frequency_nominal has been dropped
def test_convert_ecs_template_ek60(ecs_path):
# Test converting an EV calibration file (ECS)
ecs_path = ecs_path / "Ex60_Ex70_EK15_nohash.ecs"
ecs = ECSParser(ecs_path)
ecs.parse()
# Spot test parsed outcome
assert ecs.data_type == "Ex60_Ex70_EK15"
assert ecs.version == "1.00"
assert ecs.file_creation_time == datetime(
year=2023, month=3, day=16, hour=21, minute=38, second=58
)
# Apply ECS hierarchy
dict_ev_params = ecs.get_cal_params()
# Convert dict to xr.DataArray
ds_env, ds_cal, _ = ecs_ev2ep(dict_ev_params, "EK60")
# Conform to specific channel/frequency order
freq_ref = xr.DataArray(
[38000, 18000, 120000, 70000, 200000],
coords={"channel": ["chB", "chA", "chD", "chC", "chE"]},
dims=["channel"],
)
ds_cal_reorder = conform_channel_order(ds_cal, freq_ref)
ds_env_reorder = conform_channel_order(ds_env, freq_ref)
# Check reordered values
for p_name in ds_cal_reorder.data_vars:
assert np.all(ds_cal[p_name].values[[1, 0, 3, 2, 4]] == ds_cal_reorder[p_name].values)
for p_name in ds_env_reorder.data_vars:
assert np.all(ds_env[p_name].values[[1, 0, 3, 2, 4]] == ds_env_reorder[p_name].values) | {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], 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"/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,842 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/__init__.py | """
EchoData is an object that handles interfacing raw converted data.
It is used for calibration and other processing.
"""
from . import convention
from .echodata import EchoData
__all__ = ["EchoData", "convention"]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,843 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/convert/test_convert_ek80.py | import pytest
import numpy as np
import pandas as pd
from scipy.io import loadmat
from echopype import open_raw
from echopype.testing import TEST_DATA_FOLDER
from echopype.convert.set_groups_ek80 import WIDE_BAND_TRANS, PULSE_COMPRESS, FILTER_IMAG, FILTER_REAL, DECIMATION
@pytest.fixture
def ek80_path(test_path):
return test_path["EK80"]
def pytest_generate_tests(metafunc):
ek80_new_path = TEST_DATA_FOLDER / "ek80_new"
ek80_new_files = ek80_new_path.glob("**/*.raw")
if "ek80_new_file" in metafunc.fixturenames:
metafunc.parametrize(
"ek80_new_file", ek80_new_files, ids=lambda f: str(f.name)
)
@pytest.fixture
def ek80_new_file(request):
return request.param
# raw_path_simrad = ['./echopype/test_data/ek80/simrad/EK80_SimradEcho_WC381_Sequential-D20150513-T090935.raw',
# './echopype/test_data/ek80/simrad/EK80_SimradEcho_WC381_Sequential-D20150513-T091004.raw',
# './echopype/test_data/ek80/simrad/EK80_SimradEcho_WC381_Sequential-D20150513-T091034.raw',
# './echopype/test_data/ek80/simrad/EK80_SimradEcho_WC381_Sequential-D20150513-T091105.raw']
# raw_paths = ['./echopype/test_data/ek80/Summer2018--D20180905-T033113.raw',
# './echopype/test_data/ek80/Summer2018--D20180905-T033258.raw'] # Multiple files (CW and BB)
def check_env_xml(echodata):
# check environment xml datagram
# check env vars
env_vars = {
"sound_velocity_source": ["Manual", "Calculated"],
"transducer_name": ["Unknown"],
}
for env_var, expected_env_var_values in env_vars.items():
assert env_var in echodata["Environment"]
assert echodata["Environment"][env_var].dims == ("time1",)
assert all([env_var_value in expected_env_var_values for env_var_value in echodata["Environment"][env_var]])
assert "transducer_sound_speed" in echodata["Environment"]
assert echodata["Environment"]["transducer_sound_speed"].dims == ("time1",)
assert (1480 <= echodata["Environment"]["transducer_sound_speed"]).all() and (echodata["Environment"]["transducer_sound_speed"] <= 1500).all()
assert "sound_velocity_profile" in echodata["Environment"]
assert echodata["Environment"]["sound_velocity_profile"].dims == ("time1", "sound_velocity_profile_depth")
assert (1470 <= echodata["Environment"]["sound_velocity_profile"]).all() and (echodata["Environment"]["sound_velocity_profile"] <= 1500).all()
# check env dims
assert "time1" in echodata["Environment"]
assert "sound_velocity_profile_depth"
assert np.array_equal(echodata["Environment"]["sound_velocity_profile_depth"], [1, 1000])
# check a subset of platform variables. plat_vars specifies a list of possible, expected scalar values
# for each variable. The variables from the EchoData object are tested against this dictionary
# to verify their presence and their scalar values
plat_vars = {
"drop_keel_offset": [np.nan, 0, 7.5],
"drop_keel_offset_is_manual": [0, 1],
"water_level": [0],
"water_level_draft_is_manual": [0, 1]
}
for plat_var, expected_plat_var_values in plat_vars.items():
assert plat_var in echodata["Platform"]
if np.isnan(expected_plat_var_values).all():
assert np.isnan(echodata["Platform"][plat_var]).all()
else:
assert echodata["Platform"][plat_var] in expected_plat_var_values
# check plat dims
assert "time1" in echodata["Platform"]
assert "time2" in echodata["Platform"]
def test_convert(ek80_new_file, dump_output_dir):
print("converting", ek80_new_file)
echodata = open_raw(raw_file=str(ek80_new_file), sonar_model="EK80")
echodata.to_netcdf(save_path=dump_output_dir, overwrite=True)
nc_file = (dump_output_dir / ek80_new_file.name).with_suffix('.nc')
assert nc_file.is_file() is True
nc_file.unlink()
check_env_xml(echodata)
def test_convert_ek80_complex_matlab(ek80_path):
"""Compare parsed EK80 CW power/angle data with Matlab parsed data."""
ek80_raw_path_bb = str(ek80_path.joinpath('D20170912-T234910.raw'))
ek80_matlab_path_bb = str(
ek80_path.joinpath('from_matlab', 'D20170912-T234910_data.mat')
)
# Convert file
echodata = open_raw(raw_file=ek80_raw_path_bb, sonar_model='EK80')
# check water_level
assert (echodata["Platform"]["water_level"] == 0).all()
# Test complex parsed data
ds_matlab = loadmat(ek80_matlab_path_bb)
assert np.array_equal(
(
echodata["Sonar/Beam_group1"].backscatter_r
.sel(channel='WBT 549762-15 ES70-7C')
.isel(ping_time=0)
.dropna('range_sample').squeeze().values[1:, :] # squeeze remove ping_time dimension
),
np.real(
ds_matlab['data']['echodata'][0][0][0, 0]['complexsamples']
), # real part
)
assert np.array_equal(
(
echodata["Sonar/Beam_group1"].backscatter_i
.sel(channel='WBT 549762-15 ES70-7C')
.isel(ping_time=0)
.dropna('range_sample').squeeze().values[1:, :] # squeeze remove ping_time dimension
),
np.imag(
ds_matlab['data']['echodata'][0][0][0, 0]['complexsamples']
), # imag part
)
check_env_xml(echodata)
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
def test_convert_ek80_cw_power_angle_echoview(ek80_path):
"""Compare parsed EK80 CW power/angle data with csv exported by EchoView."""
ek80_raw_path_cw = str(
ek80_path.joinpath('D20190822-T161221.raw')
) # Small file (CW)
freq_list = [18, 38, 70, 120, 200]
ek80_echoview_power_csv = [
ek80_path.joinpath(
'from_echoview', 'D20190822-T161221', '%dkHz.power.csv' % freq
)
for freq in freq_list
]
ek80_echoview_angle_csv = [
ek80_path.joinpath(
'from_echoview', 'D20190822-T161221', '%dkHz.angles.points.csv' % freq
)
for freq in freq_list
]
# Convert file
echodata = open_raw(ek80_raw_path_cw, sonar_model='EK80')
# get indices of sorted frequency_nominal values. This is necessary
# because the frequency_nominal values are not always in ascending order.
sorted_freq_ind = np.argsort(echodata["Sonar/Beam_group1"].frequency_nominal)
# get sorted channel list based on frequency_nominal values
channel_list = echodata["Sonar/Beam_group1"].channel[sorted_freq_ind.values]
# check water_level
assert (echodata["Platform"]["water_level"] == 0).all()
# Test power
# single point error in original raw data. Read as -2000 by echopype and -999 by EchoView
echodata["Sonar/Beam_group1"].backscatter_r[sorted_freq_ind.values[3], 4, 13174] = -999
for file, chan in zip(ek80_echoview_power_csv, channel_list):
test_power = pd.read_csv(file, delimiter=';').iloc[:, 13:].values
assert np.allclose(
test_power,
echodata["Sonar/Beam_group1"].backscatter_r.sel(channel=chan).dropna('range_sample'),
rtol=0,
atol=1.1e-5,
)
# Convert from electrical angles to physical angle [deg]
major = (
echodata["Sonar/Beam_group1"]['angle_athwartship']
* 1.40625
/ echodata["Sonar/Beam_group1"]['angle_sensitivity_athwartship']
- echodata["Sonar/Beam_group1"]['angle_offset_athwartship']
)
minor = (
echodata["Sonar/Beam_group1"]['angle_alongship']
* 1.40625
/ echodata["Sonar/Beam_group1"]['angle_sensitivity_alongship']
- echodata["Sonar/Beam_group1"]['angle_offset_alongship']
)
for chan, file in zip(channel_list, ek80_echoview_angle_csv):
df_angle = pd.read_csv(file)
# NB: EchoView exported data only has 6 pings, but raw data actually has 7 pings.
# The first raw ping (ping 0) was removed in EchoView for some reason.
# Therefore the comparison will use ping 1-6.
for ping_idx in df_angle['Ping_index'].value_counts().index:
assert np.allclose(
df_angle.loc[df_angle['Ping_index'] == ping_idx, ' Major'],
major.sel(channel=chan)
.isel(ping_time=ping_idx)
.dropna('range_sample'),
rtol=0,
atol=5e-5,
)
assert np.allclose(
df_angle.loc[df_angle['Ping_index'] == ping_idx, ' Minor'],
minor.sel(channel=chan)
.isel(ping_time=ping_idx)
.dropna('range_sample'),
rtol=0,
atol=5e-5,
)
check_env_xml(echodata)
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
assert "transducer_offset_z" in echodata["Platform"]
assert (echodata["Platform"]["transducer_offset_z"] == 9.15).all()
def test_convert_ek80_complex_echoview(ek80_path):
"""Compare parsed EK80 BB data with csv exported by EchoView."""
ek80_raw_path_bb = ek80_path.joinpath('D20170912-T234910.raw')
ek80_echoview_bb_power_csv = ek80_path.joinpath(
'from_echoview', 'D20170912-T234910', '70 kHz raw power.complex.csv'
)
# Convert file
echodata = open_raw(raw_file=ek80_raw_path_bb, sonar_model='EK80')
# check water_level
assert (echodata["Platform"]["water_level"] == 0).all()
# Test complex parsed data
df_bb = pd.read_csv(
ek80_echoview_bb_power_csv, header=None, skiprows=[0]
) # averaged across beams
assert np.allclose(
echodata["Sonar/Beam_group1"].backscatter_r.sel(channel='WBT 549762-15 ES70-7C')
.dropna('range_sample')
.mean(dim='beam'),
df_bb.iloc[::2, 14:], # real rows
rtol=0,
atol=8e-6,
)
assert np.allclose(
echodata["Sonar/Beam_group1"].backscatter_i.sel(channel='WBT 549762-15 ES70-7C')
.dropna('range_sample')
.mean(dim='beam'),
df_bb.iloc[1::2, 14:], # imag rows
rtol=0,
atol=4e-6,
)
check_env_xml(echodata)
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
def test_convert_ek80_cw_bb_in_single_file(ek80_path):
"""Make sure can convert a single EK80 file containing both CW and BB mode data."""
ek80_raw_path_bb_cw = str(
ek80_path.joinpath('Summer2018--D20180905-T033113.raw')
)
echodata = open_raw(raw_file=ek80_raw_path_bb_cw, sonar_model='EK80')
# Check there are both Sonar/Beam_group1 and /Sonar/Beam_power groups in the converted file
assert echodata["Sonar/Beam_group2"]
assert echodata["Sonar/Beam_group1"]
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
# check water_level
assert (echodata["Platform"]["water_level"] == 0).all()
check_env_xml(echodata)
def test_convert_ek80_freq_subset(ek80_path):
"""Make sure we can convert EK80 file with multiple frequency channels off."""
ek80_raw_path_freq_subset = str(
ek80_path.joinpath('2019118 group2survey-D20191214-T081342.raw')
)
echodata = open_raw(raw_file=ek80_raw_path_freq_subset, sonar_model='EK80')
# Check if converted output has only 2 frequency channels
assert echodata["Sonar/Beam_group1"].channel.size == 2
# check platform
nan_plat_vars = [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z"
]
for plat_var in nan_plat_vars:
assert plat_var in echodata["Platform"]
assert np.isnan(echodata["Platform"][plat_var]).all()
zero_plat_vars = [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
for plat_var in zero_plat_vars:
assert plat_var in echodata["Platform"]
assert (echodata["Platform"][plat_var] == 0).all()
# check water_level
assert (echodata["Platform"]["water_level"] == 0).all()
check_env_xml(echodata)
def test_convert_ek80_raw4(ek80_path):
"""Make sure we can convert EK80 file with RAW4 datagram.."""
ek80_raw_path_freq_subset = str(
ek80_path.joinpath('raw4-D20220514-T172704.raw')
)
echodata = open_raw(raw_file=ek80_raw_path_freq_subset, sonar_model='EK80')
# Check if correct data variables exist in Beam_group1
assert "transmit_sample" in echodata["Sonar/Beam_group1"]
for var in ["transmit_pulse_r", "transmit_pulse_i"]:
assert var in echodata["Sonar/Beam_group1"]
assert echodata["Sonar/Beam_group1"][var].dims == (
'channel', 'ping_time', 'transmit_sample'
)
def test_convert_ek80_no_fil_coeff(ek80_path):
"""Make sure we can convert EK80 file with empty filter coefficients."""
echodata = open_raw(raw_file=ek80_path.joinpath('D20210330-T123857.raw'), sonar_model='EK80')
vendor_spec_ds = echodata["Vendor_specific"]
for t in [WIDE_BAND_TRANS, PULSE_COMPRESS]:
for p in [FILTER_REAL, FILTER_IMAG, DECIMATION]:
assert f"{t}_{p}" not in vendor_spec_ds
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"/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,844 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/set_groups_ek80.py | from collections import defaultdict
from typing import Dict, List, Union
import numpy as np
import xarray as xr
from numpy.typing import NDArray
from ..utils.coding import set_time_encodings
from ..utils.log import _init_logger
from .set_groups_base import SetGroupsBase
logger = _init_logger(__name__)
WIDE_BAND_TRANS = "WBT"
PULSE_COMPRESS = "PC"
FILTER_IMAG = "filter_i"
FILTER_REAL = "filter_r"
DECIMATION = "decimation"
class SetGroupsEK80(SetGroupsBase):
"""Class for saving groups to netcdf or zarr from EK80 data files."""
# The sets beam_only_names, ping_time_only_names, and
# beam_ping_time_names are used in set_groups_base and
# in converting from v0.5.x to v0.6.0. The values within
# these sets are applied to all Sonar/Beam_groupX groups.
# 2023-07-24:
# PRs:
# - https://github.com/OSOceanAcoustics/echopype/pull/1056
# - https://github.com/OSOceanAcoustics/echopype/pull/1083
# The artificially added beam and ping_time dimensions at v0.6.0
# were reverted at v0.8.0, due to concerns with efficiency and code clarity
# (see https://github.com/OSOceanAcoustics/echopype/issues/684 and
# https://github.com/OSOceanAcoustics/echopype/issues/978).
# However, the mechanisms to expand these dimensions were preserved for
# flexibility and potential later use.
# Note such expansion is still applied on AZFP data for 2 variables
# (see set_groups_azfp.py).
# Variables that need only the beam dimension added to them.
beam_only_names = set()
# Variables that need only the ping_time dimension added to them.
ping_time_only_names = set()
# Variables that need beam and ping_time dimensions added to them.
beam_ping_time_names = set()
beamgroups_possible = [
{
"name": "Beam_group1",
"descr": {
"power": "contains backscatter power (uncalibrated) and "
"other beam or channel-specific data,"
" including split-beam angle data when they exist.",
"complex": "contains complex backscatter data and other "
"beam or channel-specific data.",
},
},
{
"name": "Beam_group2",
"descr": (
"contains backscatter power (uncalibrated) and other beam or channel-specific data," # noqa
" including split-beam angle data when they exist."
),
},
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# if we have zarr files, create parser_obj.ch_ids
if self.parsed2zarr_obj.temp_zarr_dir:
for k, v in self.parsed2zarr_obj.p2z_ch_ids.items():
self.parser_obj.ch_ids[k] = self._get_channel_ids(v)
# obtain sorted channel dict in ascending order for each usage scenario
self.sorted_channel = {
"all": self._sort_list(list(self.parser_obj.config_datagram["configuration"].keys())),
"power": self._sort_list(self.parser_obj.ch_ids["power"]),
"complex": self._sort_list(self.parser_obj.ch_ids["complex"]),
"power_complex": self._sort_list(
self.parser_obj.ch_ids["power"] + self.parser_obj.ch_ids["complex"]
),
"angle": self._sort_list(self.parser_obj.ch_ids["angle"]),
}
@staticmethod
def _sort_list(list_in: List[str]) -> List[str]:
"""
Sorts a list in ascending order and then returns
the sorted list.
Parameters
----------
list_in: List[str]
List to be sorted
Returns
-------
List[str]
A copy of the input list in ascending order
"""
# make copy so we don't directly modify input list
list_in_copy = list_in.copy()
# sort list in ascending order
list_in_copy.sort(reverse=False)
return list_in_copy
def set_env(self) -> xr.Dataset:
"""Set the Environment group."""
# set time1 if it exists
if "timestamp" in self.parser_obj.environment:
time1 = np.array([self.parser_obj.environment["timestamp"]])
else:
time1 = np.array([np.datetime64("NaT")])
# Collect variables
dict_env = dict()
for k, v in self.parser_obj.environment.items():
if k in ["temperature", "depth", "acidity", "salinity", "sound_speed"]:
dict_env[k] = (["time1"], [v])
# Rename to conform with those defined in convention
if "sound_speed" in dict_env:
dict_env["sound_speed_indicative"] = dict_env.pop("sound_speed")
for k in [
"sound_absorption",
"absorption",
]: # add possible variation until having example
if k in dict_env:
dict_env["absorption_indicative"] = dict_env.pop(k)
if "sound_velocity_profile" in self.parser_obj.environment:
dict_env["sound_velocity_profile"] = (
["time1", "sound_velocity_profile_depth"],
[self.parser_obj.environment["sound_velocity_profile"][1::2]],
{
"long_name": "sound velocity profile",
"standard_name": "speed_of_sound_in_sea_water",
"units": "m/s",
"valid_min": 0.0,
"comment": "parsed from raw data files as (depth, sound_speed) value pairs",
},
)
varnames = ["sound_velocity_source", "transducer_name", "transducer_sound_speed"]
for vn in varnames:
if vn in self.parser_obj.environment:
dict_env[vn] = (
["time1"],
[self.parser_obj.environment[vn]],
)
ds = xr.Dataset(
dict_env,
coords={
"time1": (
["time1"],
time1,
{
"axis": "T",
"long_name": "Timestamps for NMEA position datagrams",
"standard_name": "time",
"comment": "Time coordinate corresponding to environmental "
"variables. Note that Platform.time3 is the same "
"as Environment.time1.",
},
),
"sound_velocity_profile_depth": (
["sound_velocity_profile_depth"],
self.parser_obj.environment["sound_velocity_profile"][::2]
if "sound_velocity_profile" in self.parser_obj.environment
else [],
{
"standard_name": "depth",
"units": "m",
"axis": "Z",
"positive": "down",
"valid_min": 0.0,
},
),
},
)
return set_time_encodings(ds)
def set_sonar(self, beam_group_type: list = ["power", None]) -> xr.Dataset:
# Collect unique variables
params = [
"transducer_frequency",
"serial_number",
"transducer_name",
"transducer_serial_number",
"application_name",
"application_version",
"channel_id_short",
]
var = defaultdict(list)
# collect all variables in params
for ch_id in self.sorted_channel["all"]:
data = self.parser_obj.config_datagram["configuration"][ch_id]
for param in params:
var[param].append(data[param])
# obtain the correct beam_group and corresponding description from beamgroups_possible
for idx, beam in enumerate(beam_group_type):
if beam is None:
# obtain values from an element where the key 'descr' does not have keys
self._beamgroups.append(self.beamgroups_possible[idx])
else:
# obtain values from an element where the key 'descr' DOES have keys
self._beamgroups.append(
{
"name": self.beamgroups_possible[idx]["name"],
"descr": self.beamgroups_possible[idx]["descr"][beam],
}
)
# Add beam_group and beam_group_descr variables sharing a common dimension
# (beam_group), using the information from self._beamgroups
beam_groups_vars, beam_groups_coord = self._beam_groups_vars()
# Create dataset
sonar_vars = {
"frequency_nominal": (
["channel"],
var["transducer_frequency"],
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
"transceiver_serial_number": (
["channel"],
var["serial_number"],
{
"long_name": "Transceiver serial number",
},
),
"transducer_name": (
["channel"],
var["transducer_name"],
{
"long_name": "Transducer name",
},
),
"transducer_serial_number": (
["channel"],
var["transducer_serial_number"],
{
"long_name": "Transducer serial number",
},
),
}
ds = xr.Dataset(
{**sonar_vars, **beam_groups_vars},
coords={
"channel": (
["channel"],
self.sorted_channel["all"],
self._varattrs["beam_coord_default"]["channel"],
),
**beam_groups_coord,
},
)
# Assemble sonar group global attribute dictionary
sonar_attr_dict = {
"sonar_manufacturer": "Simrad",
"sonar_model": self.sonar_model,
# transducer (sonar) serial number is not reliably stored in the EK80 raw
# data file and would be channel-dependent. For consistency with EK60,
# will not try to populate sonar_serial_number from the raw datagrams
"sonar_serial_number": "",
"sonar_software_name": var["application_name"][0],
"sonar_software_version": var["application_version"][0],
"sonar_type": "echosounder",
}
ds = ds.assign_attrs(sonar_attr_dict)
return ds
def set_platform(self) -> xr.Dataset:
"""Set the Platform group."""
freq = np.array(
[
self.parser_obj.config_datagram["configuration"][ch]["transducer_frequency"]
for ch in self.sorted_channel["power_complex"]
]
)
# Collect variables
if "water_level_draft" in self.parser_obj.environment:
water_level = self.parser_obj.environment["water_level_draft"]
else:
water_level = np.nan
logger.info("WARNING: The water_level_draft was not in the file. Value set to NaN.")
time1, msg_type, lat, lon = self._extract_NMEA_latlon()
time2 = self.parser_obj.mru.get("timestamp", None)
time2 = np.array(time2) if time2 is not None else [np.nan]
# Assemble variables into a dataset: variables filled with nan if do not exist
platform_dict = {"platform_name": "", "platform_type": "", "platform_code_ICES": ""}
ds = xr.Dataset(
{
"latitude": (["time1"], lat, self._varattrs["platform_var_default"]["latitude"]),
"longitude": (["time1"], lon, self._varattrs["platform_var_default"]["longitude"]),
"sentence_type": (
["time1"],
msg_type,
self._varattrs["platform_var_default"]["sentence_type"],
),
"pitch": (
["time2"],
np.array(self.parser_obj.mru.get("pitch", [np.nan])),
self._varattrs["platform_var_default"]["pitch"],
),
"roll": (
["time2"],
np.array(self.parser_obj.mru.get("roll", [np.nan])),
self._varattrs["platform_var_default"]["roll"],
),
"vertical_offset": (
["time2"],
np.array(self.parser_obj.mru.get("heave", [np.nan])),
self._varattrs["platform_var_default"]["vertical_offset"],
),
"water_level": (
[],
water_level,
self._varattrs["platform_var_default"]["water_level"],
),
"drop_keel_offset": (
[],
self.parser_obj.environment.get("drop_keel_offset", np.nan),
),
"drop_keel_offset_is_manual": (
[],
self.parser_obj.environment.get("drop_keel_offset_is_manual", np.nan),
),
"water_level_draft_is_manual": (
[],
self.parser_obj.environment.get("water_level_draft_is_manual", np.nan),
),
"transducer_offset_x": (
["channel"],
[
self.parser_obj.config_datagram["configuration"][ch].get(
"transducer_offset_x", np.nan
)
for ch in self.sorted_channel["power_complex"]
],
self._varattrs["platform_var_default"]["transducer_offset_x"],
),
"transducer_offset_y": (
["channel"],
[
self.parser_obj.config_datagram["configuration"][ch].get(
"transducer_offset_y", np.nan
)
for ch in self.sorted_channel["power_complex"]
],
self._varattrs["platform_var_default"]["transducer_offset_y"],
),
"transducer_offset_z": (
["channel"],
[
self.parser_obj.config_datagram["configuration"][ch].get(
"transducer_offset_z", np.nan
)
for ch in self.sorted_channel["power_complex"]
],
self._varattrs["platform_var_default"]["transducer_offset_z"],
),
**{
var: ([], np.nan, self._varattrs["platform_var_default"][var])
for var in [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z",
]
},
"frequency_nominal": (
["channel"],
freq,
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
},
coords={
"channel": (
["channel"],
self.sorted_channel["power_complex"],
self._varattrs["beam_coord_default"]["channel"],
),
"time1": (
["time1"],
time1,
{
**self._varattrs["platform_coord_default"]["time1"],
"comment": "Time coordinate corresponding to NMEA position data.",
},
),
"time2": (
["time2"],
time2,
{
"axis": "T",
"long_name": "Timestamps for platform motion and orientation data",
"standard_name": "time",
"comment": "Time coordinate corresponding to platform motion and "
"orientation data.",
},
),
},
)
ds = ds.assign_attrs(platform_dict)
return set_time_encodings(ds)
def _assemble_ds_ping_invariant(self, params, data_type):
"""Assemble dataset for ping-invariant params in the /Sonar/Beam_group1 group.
Parameters
----------
data_type : str
'complex' or 'power'
params : dict
beam parameters that do not change across ping
"""
freq = np.array(
[
self.parser_obj.config_datagram["configuration"][ch]["transducer_frequency"]
for ch in self.sorted_channel[data_type]
]
)
beam_params = defaultdict()
for param in params:
beam_params[param] = [
self.parser_obj.config_datagram["configuration"][ch].get(param, np.nan)
for ch in self.sorted_channel[data_type]
]
for i, ch in enumerate(self.sorted_channel[data_type]):
if (
np.isclose(beam_params["transducer_alpha_x"][i], 0.00)
and np.isclose(beam_params["transducer_alpha_y"][i], 0.00)
and np.isclose(beam_params["transducer_alpha_z"][i], 0.00)
):
beam_params["transducer_alpha_x"][i] = np.nan
beam_params["transducer_alpha_y"][i] = np.nan
beam_params["transducer_alpha_z"][i] = np.nan
ds = xr.Dataset(
{
"frequency_nominal": (
["channel"],
freq,
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
"beam_type": (
["channel"],
beam_params["transducer_beam_type"],
{"long_name": "type of transducer (0-single, 1-split)"},
),
"beamwidth_twoway_alongship": (
["channel"],
beam_params["beam_width_alongship"],
{
"long_name": "Half power two-way beam width along alongship axis of beam", # noqa
"units": "arc_degree",
"valid_range": (0.0, 360.0),
"comment": (
"Introduced in echopype for Simrad echosounders to avoid potential confusion with convention definitions. " # noqa
"The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. " # noqa
"The convention defines one-way transmit or receive beamwidth (beamwidth_receive_minor and beamwidth_transmit_minor), but Simrad echosounders record two-way beamwidth in the data." # noqa
),
},
),
"beamwidth_twoway_athwartship": (
["channel"],
beam_params["beam_width_athwartship"],
{
"long_name": "Half power two-way beam width along athwartship axis of beam", # noqa
"units": "arc_degree",
"valid_range": (0.0, 360.0),
"comment": (
"Introduced in echopype for Simrad echosounders to avoid potential confusion with convention definitions. " # noqa
"The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. " # noqa
"The convention defines one-way transmit or receive beamwidth (beamwidth_receive_major and beamwidth_transmit_major), but Simrad echosounders record two-way beamwidth in the data." # noqa
),
},
),
"beam_direction_x": (
["channel"],
beam_params["transducer_alpha_x"],
{
"long_name": "x-component of the vector that gives the pointing "
"direction of the beam, in sonar beam coordinate "
"system",
"units": "1",
"valid_range": (-1.0, 1.0),
},
),
"beam_direction_y": (
["channel"],
beam_params["transducer_alpha_y"],
{
"long_name": "y-component of the vector that gives the pointing "
"direction of the beam, in sonar beam coordinate "
"system",
"units": "1",
"valid_range": (-1.0, 1.0),
},
),
"beam_direction_z": (
["channel"],
beam_params["transducer_alpha_z"],
{
"long_name": "z-component of the vector that gives the pointing "
"direction of the beam, in sonar beam coordinate "
"system",
"units": "1",
"valid_range": (-1.0, 1.0),
},
),
"angle_offset_alongship": (
["channel"],
beam_params["angle_offset_alongship"],
{
"long_name": "electrical alongship angle offset of the transducer",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
"The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
"angle_offset_athwartship": (
["channel"],
beam_params["angle_offset_athwartship"],
{
"long_name": "electrical athwartship angle offset of the transducer",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
"The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
"angle_sensitivity_alongship": (
["channel"],
beam_params["angle_sensitivity_alongship"],
{
"long_name": "alongship angle sensitivity of the transducer",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
"The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
"angle_sensitivity_athwartship": (
["channel"],
beam_params["angle_sensitivity_athwartship"],
{
"long_name": "athwartship angle sensitivity of the transducer",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
"The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
"equivalent_beam_angle": (
["channel"],
beam_params["equivalent_beam_angle"],
{
"long_name": "Equivalent beam angle",
"units": "sr",
"valid_range": (0.0, 4 * np.pi),
},
),
"transceiver_software_version": (
["channel"],
beam_params["transceiver_software_version"],
),
"beam_stabilisation": (
[],
np.array(0, np.byte),
{
"long_name": "Beam stabilisation applied (or not)",
"flag_values": [0, 1],
"flag_meanings": ["not stabilised", "stabilised"],
},
),
"non_quantitative_processing": (
[],
np.array(0, np.int16),
{
"long_name": "Presence or not of non-quantitative processing applied"
" to the backscattering data (sonar specific)",
"flag_values": [0],
"flag_meanings": ["None"],
},
),
},
coords={
"channel": (
["channel"],
self.sorted_channel[data_type],
self._varattrs["beam_coord_default"]["channel"],
),
},
attrs={"beam_mode": "vertical", "conversion_equation_t": "type_3"},
)
if data_type == "power":
ds = ds.assign(
{
"transmit_frequency_start": (
["channel"],
freq,
self._varattrs["beam_var_default"]["transmit_frequency_start"],
),
"transmit_frequency_stop": (
["channel"],
freq,
self._varattrs["beam_var_default"]["transmit_frequency_stop"],
),
}
)
return ds
def _add_freq_start_end_ds(self, ds_tmp: xr.Dataset, ch: str) -> xr.Dataset:
"""
Returns a Dataset with variables
``transmit_frequency_start`` and ``transmit_frequency_stop``
added to ``ds_tmp`` for a specific channel.
Parameters
----------
ds_tmp: xr.Dataset
Dataset containing the complex data
ch: str
Channel id
"""
# Process if it's a BB channel (not all pings are CW, where pulse_form encodes CW as 0)
# CW data encoded as complex samples do NOT have frequency_start and frequency_end
if not np.all(np.array(self.parser_obj.ping_data_dict["pulse_form"][ch]) == 0):
freq_start = np.array(self.parser_obj.ping_data_dict["frequency_start"][ch])
freq_stop = np.array(self.parser_obj.ping_data_dict["frequency_end"][ch])
elif not self.sorted_channel["power"]:
freq = self.parser_obj.config_datagram["configuration"][ch]["transducer_frequency"]
freq_start = np.full(len(self.parser_obj.ping_time[ch]), freq)
freq_stop = freq_start
else:
return ds_tmp
ds_f_start_end = xr.Dataset(
{
"transmit_frequency_start": (
["ping_time"],
freq_start.astype(float),
self._varattrs["beam_var_default"]["transmit_frequency_start"],
),
"transmit_frequency_stop": (
["ping_time"],
freq_stop.astype(float),
self._varattrs["beam_var_default"]["transmit_frequency_stop"],
),
},
coords={
"ping_time": (
["ping_time"],
self.parser_obj.ping_time[ch],
self._varattrs["beam_coord_default"]["ping_time"],
),
},
)
ds_tmp = xr.merge(
[ds_tmp, ds_f_start_end], combine_attrs="override"
) # override keeps the Dataset attributes
return ds_tmp
def _assemble_ds_complex(self, ch):
num_transducer_sectors = np.unique(
np.array(self.parser_obj.ping_data_dict["n_complex"][ch])
)
if num_transducer_sectors.size > 1: # this is not supposed to happen
raise ValueError("Transducer sector number changes in the middle of the file!")
else:
num_transducer_sectors = num_transducer_sectors[0]
data_shape = self.parser_obj.ping_data_dict["complex"][ch].shape
data_shape = (
data_shape[0],
int(data_shape[1] / num_transducer_sectors),
num_transducer_sectors,
)
data = self.parser_obj.ping_data_dict["complex"][ch].reshape(data_shape)
ds_tmp = xr.Dataset(
{
"backscatter_r": (
["ping_time", "range_sample", "beam"],
np.real(data),
{
"long_name": self._varattrs["beam_var_default"]["backscatter_r"][
"long_name"
],
"units": "dB",
},
),
"backscatter_i": (
["ping_time", "range_sample", "beam"],
np.imag(data),
{
"long_name": self._varattrs["beam_var_default"]["backscatter_i"][
"long_name"
],
"units": "dB",
},
),
},
coords={
"ping_time": (
["ping_time"],
self.parser_obj.ping_time[ch],
self._varattrs["beam_coord_default"]["ping_time"],
),
"range_sample": (
["range_sample"],
np.arange(data_shape[1]),
self._varattrs["beam_coord_default"]["range_sample"],
),
"beam": (
["beam"],
np.arange(start=1, stop=num_transducer_sectors + 1).astype(str),
self._varattrs["beam_coord_default"]["beam"],
),
},
)
ds_tmp = self._add_freq_start_end_ds(ds_tmp, ch)
return set_time_encodings(ds_tmp)
def _add_trasmit_pulse_complex(self, ds_tmp: xr.Dataset, ch: str) -> xr.Dataset:
"""
Adds RAW4 datagram values (transmit pulse recorded in
complex samples), if it exists, to the power and angle
data.
Parameters
----------
ds_tmp : xr.Dataset
Dataset to add the transmit data to
ch : str
Name of channel key to grab the data from
Returns
-------
ds_tmp : xr.Dataset
The input Dataset with transmit data added to it.
"""
# If RAW4 datagram (transmit pulse recorded in complex samples) exists
if (len(self.parser_obj.ping_data_dict_tx["complex"]) != 0) and (
ch in self.parser_obj.ping_data_dict_tx["complex"].keys()
):
# Add coordinate transmit_sample
ds_tmp = ds_tmp.assign_coords(
{
"transmit_sample": (
["transmit_sample"],
np.arange(self.parser_obj.ping_data_dict_tx["complex"][ch].shape[1]),
{
"long_name": "Transmit pulse sample number, base 0",
"comment": "Only exist for Simrad EK80 file with RAW4 datagrams",
},
),
},
)
# Add data variables transmit_pulse_r/i
ds_tmp = ds_tmp.assign(
{
"transmit_pulse_r": (
["ping_time", "transmit_sample"],
np.real(self.parser_obj.ping_data_dict_tx["complex"][ch]),
{
"long_name": "Real part of the transmit pulse",
"units": "V",
"comment": "Only exist for Simrad EK80 file with RAW4 datagrams",
},
),
"transmit_pulse_i": (
["ping_time", "transmit_sample"],
np.imag(self.parser_obj.ping_data_dict_tx["complex"][ch]),
{
"long_name": "Imaginary part of the transmit pulse",
"units": "V",
"comment": "Only exist for Simrad EK80 file with RAW4 datagrams",
},
),
},
)
return ds_tmp
def _assemble_ds_power(self, ch):
ds_tmp = xr.Dataset(
{
"backscatter_r": (
["ping_time", "range_sample"],
self.parser_obj.ping_data_dict["power"][ch],
{
"long_name": self._varattrs["beam_var_default"]["backscatter_r"][
"long_name"
],
"units": "dB",
},
),
},
coords={
"ping_time": (
["ping_time"],
self.parser_obj.ping_time[ch],
self._varattrs["beam_coord_default"]["ping_time"],
),
"range_sample": (
["range_sample"],
np.arange(self.parser_obj.ping_data_dict["power"][ch].shape[1]),
self._varattrs["beam_coord_default"]["range_sample"],
),
},
)
ds_tmp = self._add_trasmit_pulse_complex(ds_tmp, ch)
# If angle data exist
if ch in self.sorted_channel["angle"]:
ds_tmp = ds_tmp.assign(
{
"angle_athwartship": (
["ping_time", "range_sample"],
self.parser_obj.ping_data_dict["angle"][ch][:, :, 0],
{
"long_name": "electrical athwartship angle",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
+ "The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
"angle_alongship": (
["ping_time", "range_sample"],
self.parser_obj.ping_data_dict["angle"][ch][:, :, 1],
{
"long_name": "electrical alongship angle",
"comment": (
"Introduced in echopype for Simrad echosounders. " # noqa
+ "The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. " # noqa
),
},
),
}
)
ds_tmp = self._add_freq_start_end_ds(ds_tmp, ch)
return set_time_encodings(ds_tmp)
def _assemble_ds_common(self, ch, range_sample_size):
"""Variables common to complex and power/angle data."""
# pulse duration may have different names
if "pulse_length" in self.parser_obj.ping_data_dict:
pulse_length = np.array(
self.parser_obj.ping_data_dict["pulse_length"][ch], dtype="float32"
)
else:
pulse_length = np.array(
self.parser_obj.ping_data_dict["pulse_duration"][ch], dtype="float32"
)
def pulse_form_map(pulse_form):
str_map = np.array(["CW", "LFM", "", "", "", "FMD"])
return str_map[pulse_form]
ds_common = xr.Dataset(
{
"sample_interval": (
["ping_time"],
self.parser_obj.ping_data_dict["sample_interval"][ch],
{
"long_name": "Interval between recorded raw data samples",
"units": "s",
"valid_min": 0.0,
},
),
"transmit_power": (
["ping_time"],
self.parser_obj.ping_data_dict["transmit_power"][ch],
{
"long_name": "Nominal transmit power",
"units": "W",
"valid_min": 0.0,
},
),
"transmit_duration_nominal": (
["ping_time"],
pulse_length,
{
"long_name": "Nominal bandwidth of transmitted pulse",
"units": "s",
"valid_min": 0.0,
},
),
"slope": (
["ping_time"],
self.parser_obj.ping_data_dict["slope"][ch],
{"long_name": "Hann window slope parameter for transmit signal"},
),
"channel_mode": (
["ping_time"],
np.array(self.parser_obj.ping_data_dict["channel_mode"][ch], dtype=np.byte),
{
"long_name": "Transceiver mode",
"flag_values": [0, 1],
"flag_meanings": ["Active", "Unknown"],
},
),
"transmit_type": (
["ping_time"],
pulse_form_map(np.array(self.parser_obj.ping_data_dict["pulse_form"][ch])),
{
"long_name": "Type of transmitted pulse",
"flag_values": ["CW", "LFM", "FMD"],
"flag_meanings": [
"Continuous Wave – a pulse nominally of one frequency",
"Linear Frequency Modulation – a pulse which varies from "
"transmit_frequency_start to transmit_frequency_stop in a linear "
"manner over the nominal pulse duration (transmit_duration_nominal)",
"Frequency Modulated 'D' - An EK80-specific FM type that is not "
"clearly described",
],
},
),
"sample_time_offset": (
["ping_time"],
(
np.array(self.parser_obj.ping_data_dict["offset"][ch])
* np.array(self.parser_obj.ping_data_dict["sample_interval"][ch])
),
{
"long_name": "Time offset that is subtracted from the timestamp"
" of each sample",
"units": "s",
},
),
},
coords={
"ping_time": (
["ping_time"],
self.parser_obj.ping_time[ch],
self._varattrs["beam_coord_default"]["ping_time"],
),
"range_sample": (
["range_sample"],
np.arange(range_sample_size),
self._varattrs["beam_coord_default"]["range_sample"],
),
},
)
return set_time_encodings(ds_common)
@staticmethod
def merge_save(ds_combine: List[xr.Dataset], ds_invariant: xr.Dataset) -> xr.Dataset:
"""Merge data from all complex or all power/angle channels"""
ds_combine = xr.merge(ds_combine)
ds_combine = xr.merge(
[ds_invariant, ds_combine], combine_attrs="override"
) # override keeps the Dataset attributes
return set_time_encodings(ds_combine)
def _attach_vars_to_ds_data(self, ds_data: xr.Dataset, ch: str, rs_size: int) -> xr.Dataset:
"""
Attaches common variables and the channel dimension.
Parameters
----------
ds_data : xr.Dataset
Data set to add variables to
ch: str
Channel string associated with variables
rs_size: int
The size of the range sample dimension
i.e. ``range_sample.size``
Returns
-------
``ds_data`` with the variables added to it.
"""
ds_common = self._assemble_ds_common(ch, rs_size)
ds_data = xr.merge([ds_data, ds_common], combine_attrs="override")
# Attach channel dimension/coordinate
ds_data = ds_data.expand_dims(
{"channel": [self.parser_obj.config_datagram["configuration"][ch]["channel_id"]]}
)
ds_data["channel"] = ds_data["channel"].assign_attrs(
**self._varattrs["beam_coord_default"]["channel"]
)
return ds_data
def _get_ds_beam_power_zarr(self, ds_invariant_power: xr.Dataset) -> xr.Dataset:
"""
Constructs the data set `ds_beam_power` when
there are zarr variables present.
Parameters
----------
ds_invariant_power : xr.Dataset
Dataset for ping-invariant params associated with power
Returns
-------
A Dataset representing `ds_beam_power`.
"""
# TODO: In the future it would be nice to have a dictionary of
# attributes stored in one place for all of the variables.
# This would reduce unnecessary code duplication in the
# functions below.
# obtain DataArrays using zarr variables
zarr_path = self.parsed2zarr_obj.zarr_file_name
backscatter_r = self._get_power_dataarray(zarr_path)
angle_athwartship, angle_alongship = self._get_angle_dataarrays(zarr_path)
# create power related ds using DataArrays created from zarr file
ds_power = xr.merge([backscatter_r, angle_athwartship, angle_alongship])
ds_power = set_time_encodings(ds_power)
# obtain additional variables that need to be added to ds_power
ds_tmp = []
for ch in self.sorted_channel["power"]:
ds_data = self._add_trasmit_pulse_complex(ds_tmp=xr.Dataset(), ch=ch)
ds_data = set_time_encodings(ds_data)
ds_data = self._attach_vars_to_ds_data(ds_data, ch, rs_size=ds_power.range_sample.size)
ds_tmp.append(ds_data)
ds_tmp = self.merge_save(ds_tmp, ds_invariant_power)
return xr.merge([ds_tmp, ds_power], combine_attrs="override")
def _get_ds_complex_zarr(self, ds_invariant_complex: xr.Dataset) -> xr.Dataset:
"""
Constructs the data set `ds_complex` when
there are zarr variables present.
Parameters
----------
ds_invariant_complex : xr.Dataset
Dataset for ping-invariant params associated with complex data
Returns
-------
A Dataset representing `ds_complex`.
"""
# TODO: In the future it would be nice to have a dictionary of
# attributes stored in one place for all of the variables.
# This would reduce unnecessary code duplication in the
# functions below.
# obtain DataArrays using zarr variables
zarr_path = self.parsed2zarr_obj.zarr_file_name
backscatter_r, backscatter_i = self._get_complex_dataarrays(zarr_path)
# create power related ds using DataArrays created from zarr file
ds_complex = xr.merge([backscatter_r, backscatter_i])
ds_complex = set_time_encodings(ds_complex)
# obtain additional variables that need to be added to ds_complex
ds_tmp = []
for ch in self.sorted_channel["complex"]:
ds_data = self._add_trasmit_pulse_complex(ds_tmp=xr.Dataset(), ch=ch)
ds_data = self._add_freq_start_end_ds(ds_data, ch)
ds_data = set_time_encodings(ds_data)
ds_data = self._attach_vars_to_ds_data(
ds_data, ch, rs_size=ds_complex.range_sample.size
)
ds_tmp.append(ds_data)
ds_tmp = self.merge_save(ds_tmp, ds_invariant_complex)
return xr.merge([ds_tmp, ds_complex], combine_attrs="override")
def set_beam(self) -> List[xr.Dataset]:
"""Set the /Sonar/Beam_group1 group."""
# Assemble ping-invariant beam data variables
params = [
"transducer_beam_type",
"beam_width_alongship",
"beam_width_athwartship",
"transducer_alpha_x",
"transducer_alpha_y",
"transducer_alpha_z",
"angle_offset_alongship",
"angle_offset_athwartship",
"angle_sensitivity_alongship",
"angle_sensitivity_athwartship",
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
"equivalent_beam_angle",
"transceiver_software_version",
]
# Assemble dataset for ping-invariant params
if self.sorted_channel["complex"]:
ds_invariant_complex = self._assemble_ds_ping_invariant(params, "complex")
if self.sorted_channel["power"]:
ds_invariant_power = self._assemble_ds_ping_invariant(params, "power")
if not self.parsed2zarr_obj.temp_zarr_dir:
# Assemble dataset for backscatter data and other ping-by-ping data
ds_complex = []
ds_power = []
for ch in self.sorted_channel["all"]:
if ch in self.sorted_channel["complex"]:
ds_data = self._assemble_ds_complex(ch)
elif ch in self.sorted_channel["power"]:
ds_data = self._assemble_ds_power(ch)
else: # skip for channels containing no data
continue
ds_data = self._attach_vars_to_ds_data(
ds_data, ch, rs_size=ds_data.range_sample.size
)
if ch in self.sorted_channel["complex"]:
ds_complex.append(ds_data)
else:
ds_power.append(ds_data)
# Merge and save group:
# if both complex and power data exist: complex data in /Sonar/Beam_group1 group
# and power data in /Sonar/Beam_group2
# if only one type of data exist: data in /Sonar/Beam_group1 group
ds_beam_power = None
if len(ds_complex) > 0:
ds_beam = self.merge_save(ds_complex, ds_invariant_complex)
if len(ds_power) > 0:
ds_beam_power = self.merge_save(ds_power, ds_invariant_power)
else:
ds_beam = self.merge_save(ds_power, ds_invariant_power)
else:
if self.sorted_channel["power"]:
ds_power = self._get_ds_beam_power_zarr(ds_invariant_power)
else:
ds_power = None
if self.sorted_channel["complex"]:
ds_complex = self._get_ds_complex_zarr(ds_invariant_complex)
else:
ds_complex = None
# correctly assign the beam groups
ds_beam_power = None
if ds_complex:
ds_beam = ds_complex
if ds_power:
ds_beam_power = ds_power
else:
ds_beam = ds_power
# Manipulate some Dataset dimensions to adhere to convention
if isinstance(ds_beam_power, xr.Dataset):
self.beam_groups_to_convention(
ds_beam_power,
self.beam_only_names,
self.beam_ping_time_names,
self.ping_time_only_names,
)
self.beam_groups_to_convention(
ds_beam, self.beam_only_names, self.beam_ping_time_names, self.ping_time_only_names
)
return [ds_beam, ds_beam_power]
def set_vendor(self) -> xr.Dataset:
"""Set the Vendor_specific group."""
config = self.parser_obj.config_datagram["configuration"]
# Channel-specific parameters
# exist for all channels:
# - sa_correction
# - gain (indexed by pulse_length)
# may not exist for data from earlier EK80 software:
# - impedance
# - receiver sampling frequency
# - transceiver type
table_params = [
"transducer_frequency",
"impedance", # transceiver impedance (z_er), different from transducer impedance (z_et)
"rx_sample_frequency", # receiver sampling frequency
"transceiver_type",
"pulse_duration",
"sa_correction",
"gain",
]
# grab all variables in table_params
param_dict = defaultdict(list)
for ch in self.sorted_channel["all"]:
v = self.parser_obj.config_datagram["configuration"][ch]
for p in table_params:
if p in v: # only for parameter that exist in configuration dict
param_dict[p].append(v[p])
# make values into numpy arrays
for p in param_dict.keys():
param_dict[p] = np.array(param_dict[p])
# Param size check
if (
not param_dict["pulse_duration"].shape
== param_dict["sa_correction"].shape
== param_dict["gain"].shape
):
raise ValueError("Narrowband calibration parameters dimension mismatch!")
ds_table = xr.Dataset(
{
"frequency_nominal": (
["channel"],
param_dict["transducer_frequency"],
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
"sa_correction": (
["channel", "pulse_length_bin"],
np.array(param_dict["sa_correction"]),
),
"gain_correction": (
["channel", "pulse_length_bin"],
np.array(param_dict["gain"]),
),
"pulse_length": (
["channel", "pulse_length_bin"],
np.array(param_dict["pulse_duration"]),
),
},
coords={
"channel": (
["channel"],
self.sorted_channel["all"],
self._varattrs["beam_coord_default"]["channel"],
),
"pulse_length_bin": (
["pulse_length_bin"],
np.arange(param_dict["pulse_duration"].shape[1]),
),
},
)
# Parameters that may or may not exist (due to EK80 software version)
if "impedance" in param_dict:
ds_table["impedance_transceiver"] = xr.DataArray(
param_dict["impedance"],
dims=["channel"],
coords={"channel": ds_table["channel"]},
attrs={
"units": "ohm",
"long_name": "Transceiver impedance",
},
)
if "rx_sample_frequency" in param_dict:
ds_table["receiver_sampling_frequency"] = xr.DataArray(
param_dict["rx_sample_frequency"].astype(float),
dims=["channel"],
coords={"channel": ds_table["channel"]},
attrs={
"units": "Hz",
"long_name": "Receiver sampling frequency",
},
)
if "transceiver_type" in param_dict:
ds_table["transceiver_type"] = xr.DataArray(
param_dict["transceiver_type"],
dims=["channel"],
coords={"channel": ds_table["channel"]},
attrs={
"long_name": "Transceiver type",
},
)
# Broadband calibration parameters: use the zero padding approach
cal_ch_ids = [
ch for ch in self.sorted_channel["all"] if "calibration" in config[ch]
] # channels with cal params
ds_cal = []
for ch_id in cal_ch_ids:
# TODO: consider using the full_ch_name below in place of channel id (ch_id)
# full_ch_name = (f"{config[ch]['transceiver_type']} " +
# f"{config[ch]['serial_number']}-" +
# f"{config[ch]['hw_channel_configuration']} " +
# f"{config[ch]['channel_id_short']}")
cal_params = [
"gain",
"impedance", # transducer impedance (z_et), different from transceiver impedance (z_er) # noqa
"phase",
"beamwidth_alongship",
"beamwidth_athwartship",
"angle_offset_alongship",
"angle_offset_athwartship",
]
param_dict = {}
for p in cal_params:
if p in config[ch_id]["calibration"]: # only for parameters that exist in dict
param_dict[p] = (["cal_frequency"], config[ch_id]["calibration"][p])
ds_ch = xr.Dataset(
data_vars=param_dict,
coords={
"cal_frequency": (
["cal_frequency"],
config[ch_id]["calibration"]["frequency"],
{
"long_name": "Frequency of calibration parameter",
"units": "Hz",
},
)
},
)
ds_ch = ds_ch.expand_dims({"cal_channel_id": [ch_id]})
ds_ch["cal_channel_id"].attrs[
"long_name"
] = "ID of channels containing broadband calibration information"
ds_cal.append(ds_ch)
ds_cal = xr.merge(ds_cal)
if "impedance" in ds_cal:
ds_cal = ds_cal.rename_vars({"impedance": "impedance_transducer"})
# Save decimation factors and filter coefficients
# Param map values
# 1: wide band transceiver (WBT)
# 2: pulse compression (PC)
param_map = {1: WIDE_BAND_TRANS, 2: PULSE_COMPRESS}
coeffs_and_decimation = {
t: {FILTER_IMAG: [], FILTER_REAL: [], DECIMATION: []} for t in list(param_map.values())
}
for ch in self.sorted_channel["all"]:
fil_coeffs = self.parser_obj.fil_coeffs.get(ch, None)
fil_df = self.parser_obj.fil_df.get(ch, None)
if fil_coeffs and fil_df:
# get filter coefficient values
for type_num, values in fil_coeffs.items():
param = param_map[type_num]
coeffs_and_decimation[param][FILTER_IMAG].append(np.imag(values))
coeffs_and_decimation[param][FILTER_REAL].append(np.real(values))
# get decimation factor values
for type_num, value in fil_df.items():
param = param_map[type_num]
coeffs_and_decimation[param][DECIMATION].append(value)
# Assemble everything into a Dataset
ds = xr.merge([ds_table, ds_cal])
# Add the coeffs and decimation
ds = ds.pipe(self._add_filter_params, coeffs_and_decimation)
# Save the entire config XML in vendor group in case of info loss
ds["config_xml"] = self.parser_obj.config_datagram["xml"]
return ds
@staticmethod
def _add_filter_params(
dataset: xr.Dataset, coeffs_and_decimation: Dict[str, Dict[str, List[Union[int, NDArray]]]]
) -> xr.Dataset:
"""
Assembles filter coefficient and decimation factors and add to the dataset
Parameters
----------
dataset : xr.Dataset
xarray dataset where the filter coefficient and decimation factors will be added
coeffs_and_decimation : dict
dictionary holding the filter coefficient and decimation factors
Returns
-------
xr.Dataset
The modified dataset with filter coefficient and decimation factors included
"""
attribute_values = {
FILTER_IMAG: "filter coefficients (imaginary part)",
FILTER_REAL: "filter coefficients (real part)",
DECIMATION: "decimation factor",
WIDE_BAND_TRANS: "Wideband transceiver",
PULSE_COMPRESS: "Pulse compression",
}
coeffs_xr_data = {}
for cd_type, values in coeffs_and_decimation.items():
for key, data in values.items():
if data:
if "filter" in key:
attrs = {
"long_name": f"{attribute_values[cd_type]} {attribute_values[key]}"
}
# filter_i and filter_r
max_len = np.max([len(a) for a in data])
# Pad arrays
data = np.asarray(
[
np.pad(a, (0, max_len - len(a)), "constant", constant_values=np.nan)
for a in data
]
)
dims = ["channel", f"{cd_type}_filter_n"]
else:
attrs = {
"long_name": f"{attribute_values[cd_type]} {attribute_values[DECIMATION]}" # noqa
}
dims = ["channel"]
# Set the xarray data dictionary
coeffs_xr_data[f"{cd_type}_{key}"] = (dims, data, attrs)
return dataset.assign(coeffs_xr_data)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], 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"/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,845 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/consolidate/api.py | import datetime
import pathlib
from typing import Optional, Union
import numpy as np
import xarray as xr
from ..calibrate.ek80_complex import get_filter_coeff
from ..echodata import EchoData
from ..echodata.simrad import retrieve_correct_beam_group
from ..utils.io import validate_source_ds_da
from ..utils.prov import add_processing_level
from .split_beam_angle import add_angle_to_ds, get_angle_complex_samples, get_angle_power_samples
def swap_dims_channel_frequency(ds: xr.Dataset) -> xr.Dataset:
"""
Use frequency_nominal in place of channel to be dataset dimension and coorindate.
This is useful because the nominal transducer frequencies are commonly used to
refer to data collected from a specific transducer.
Parameters
----------
ds : xr.Dataset
Dataset for which the dimension will be swapped
Returns
-------
The input dataset with the dimension swapped
Notes
-----
This operation is only possible when there are no duplicated frequencies present in the file.
"""
# Only possible if no duplicated frequencies
if np.unique(ds["frequency_nominal"]).size == ds["frequency_nominal"].size:
return (
ds.set_coords("frequency_nominal")
.swap_dims({"channel": "frequency_nominal"})
.reset_coords("channel")
)
else:
raise ValueError(
"Duplicated transducer nominal frequencies exist in the file. "
"Operation is not valid."
)
def add_depth(
ds: xr.Dataset,
depth_offset: float = 0,
tilt: float = 0,
downward: bool = True,
) -> xr.Dataset:
"""
Create a depth data variable based on data in Sv dataset.
The depth is generated based on whether the transducers are mounted vertically
or with a polar angle to vertical, and whether the transducers were pointed
up or down.
Parameters
----------
ds : xr.Dataset
Source Sv dataset to which a depth variable will be added.
Must contain `echo_range`.
depth_offset : float
Offset along the vertical (depth) dimension to account for actual transducer
position in water, since `echo_range` is counted from transducer surface.
Default is 0.
tilt : float
Transducer tilt angle [degree].
Default is 0 (transducer mounted vertically).
downward : bool
Whether or not the transducers point downward.
Default to True.
Returns
-------
The input dataset with a `depth` variable (in meters) added
Notes
-----
Currently this function only scalar inputs of depth_offset and tilt angle.
In future expansion we plan to add the following options:
* Allow inputs as xr.DataArray for time-varying variations of these variables
* Use data stored in the EchoData object or raw-converted file from which the Sv is derived,
specifically `water_level`, `vertical_offtset` and `tilt` in the `Platform` group.
"""
# TODO: add options to use water_depth, vertical_offset, tilt stored in EchoData
# # Water level has to come from somewhere
# if depth_offset is None:
# if "water_level" in ds:
# depth_offset = ds["water_level"]
# else:
# raise ValueError(
# "water_level not found in dataset and needs to be supplied by the user"
# )
# # If not vertical needs to have tilt
# if not vertical:
# if tilt is None:
# if "tilt" in ds:
# tilt = ds["tilt"]
# else:
# raise ValueError(
# "tilt not found in dataset and needs to be supplied by the user. "
# "Required when vertical=False"
# )
# else:
# tilt = 0
# Multiplication factor depending on if transducers are pointing downward
mult = 1 if downward else -1
# Compute depth
ds["depth"] = mult * ds["echo_range"] * np.cos(tilt / 180 * np.pi) + depth_offset
ds["depth"].attrs = {"long_name": "Depth", "standard_name": "depth", "units": "m"}
# Add history attribute
history_attr = (
f"{datetime.datetime.utcnow()} +00:00. "
"Added based on echo_range or other data in Sv dataset." # noqa
)
ds["depth"] = ds["depth"].assign_attrs({"history": history_attr})
return ds
@add_processing_level("L2A")
def add_location(ds: xr.Dataset, echodata: EchoData = None, nmea_sentence: Optional[str] = None):
"""
Add geographical location (latitude/longitude) to the Sv dataset.
This function interpolates the location from the Platform group in the original data file
based on the time when the latitude/longitude data are recorded and the time the acoustic
data are recorded (`ping_time`).
Parameters
----------
ds : xr.Dataset
An Sv or MVBS dataset for which the geographical locations will be added to
echodata
An `EchoData` object holding the raw data
nmea_sentence
NMEA sentence to select a subset of location data (optional)
Returns
-------
The input dataset with the location data added
"""
def sel_interp(var, time_dim_name):
# NMEA sentence selection
if nmea_sentence:
position_var = echodata["Platform"][var][
echodata["Platform"]["sentence_type"] == nmea_sentence
]
else:
position_var = echodata["Platform"][var]
if len(position_var) == 1:
# Propagate single, fixed-location coordinate
return xr.DataArray(
data=position_var.values[0] * np.ones(len(ds["ping_time"]), dtype=np.float64),
dims=["ping_time"],
attrs=position_var.attrs,
)
else:
# Values may be nan if there are ping_time values outside the time_dim_name range
return position_var.interp(**{time_dim_name: ds["ping_time"]})
if "longitude" not in echodata["Platform"] or echodata["Platform"]["longitude"].isnull().all():
raise ValueError("Coordinate variables not present or all nan")
interp_ds = ds.copy()
time_dim_name = list(echodata["Platform"]["longitude"].dims)[0]
interp_ds["latitude"] = sel_interp("latitude", time_dim_name)
interp_ds["longitude"] = sel_interp("longitude", time_dim_name)
# Most attributes are attached automatically via interpolation
# here we add the history
history_attr = (
f"{datetime.datetime.utcnow()} +00:00. "
"Interpolated or propagated from Platform latitude/longitude." # noqa
)
for da_name in ["latitude", "longitude"]:
interp_ds[da_name] = interp_ds[da_name].assign_attrs({"history": history_attr})
if time_dim_name in interp_ds:
interp_ds = interp_ds.drop_vars(time_dim_name)
return interp_ds
def add_splitbeam_angle(
source_Sv: Union[xr.Dataset, str, pathlib.Path],
echodata: EchoData,
waveform_mode: str,
encode_mode: str,
pulse_compression: bool = False,
storage_options: dict = {},
return_dataset: bool = True,
) -> xr.Dataset:
"""
Add split-beam (alongship/athwartship) angles into the Sv dataset.
This function calculates the alongship/athwartship angle using data stored
in the Sonar/Beam_groupX groups of an EchoData object.
In cases when angle data does not already exist or cannot be computed from the data,
an error is issued and no angle variables are added to the dataset.
Parameters
----------
source_Sv: xr.Dataset or str or pathlib.Path
The Sv Dataset or path to a file containing the Sv Dataset,
to which the split-beam angles will be added
echodata: EchoData
An ``EchoData`` object holding the raw data
waveform_mode : {"CW", "BB"}
Type of transmit waveform
- ``"CW"`` for narrowband transmission,
returned echoes recorded either as complex or power/angle samples
- ``"BB"`` for broadband transmission,
returned echoes recorded as complex samples
encode_mode : {"complex", "power"}
Type of encoded return echo data
- ``"complex"`` for complex samples
- ``"power"`` for power/angle samples, only allowed when
the echosounder is configured for narrowband transmission
pulse_compression: bool, False
Whether pulse compression should be used (only valid for
``waveform_mode="BB"`` and ``encode_mode="complex"``)
storage_options: dict, default={}
Any additional parameters for the storage backend, corresponding to the
path provided for ``source_Sv``
return_dataset: bool, default=True
If ``True``, ``source_Sv`` with split-beam angles added will be returned.
``return_dataset=False`` is useful when ``source_Sv`` is a path and
users only want to write the split-beam angle data to this path.
Returns
-------
xr.Dataset or None
If ``return_dataset=False``, nothing will be returned.
If ``return_dataset=True``, either the input dataset ``source_Sv``
or a lazy-loaded Dataset (from the path ``source_Sv``)
with split-beam angles added will be returned.
Raises
------
ValueError
If ``echodata`` has a sonar model that is not analogous to either EK60 or EK80
ValueError
If the input ``source_Sv`` does not have a ``channel`` dimension
ValueError
If ``source_Sv`` does not have appropriate dimension lengths in
comparison to ``echodata`` data
ValueError
If the provided ``waveform_mode``, ``encode_mode``, and ``pulse_compression`` are not valid
NotImplementedError
If an unknown ``beam_type`` is encountered during the split-beam calculation
Notes
-----
Split-beam angle data potentially exist for the Simrad EK60 or EK80 echosounders
with split-beam transducers and configured to store angle data (along with power samples)
or store raw complex samples.
In most cases where the type of samples collected by the echosounder (power/angle
samples or complex samples) and the transmit waveform (broadband or narrowband)
are identical across all channels, the channels existing in ``source_Sv`` and `
`echodata`` will be identical. If this is not the case, only angle data corresponding
to channels existing in ``source_Sv`` will be added.
"""
# ensure that echodata was produced by EK60 or EK80-like sensors
if echodata.sonar_model not in ["EK60", "ES70", "EK80", "ES80", "EA640"]:
raise ValueError(
"The sonar model that produced echodata does not have split-beam "
"transducers, split-beam angles cannot be added to source_Sv!"
)
# validate the source_Sv type or path (if it is provided)
source_Sv, file_type = validate_source_ds_da(source_Sv, storage_options)
# initialize source_Sv_path
source_Sv_path = None
if isinstance(source_Sv, str):
# store source_Sv path so we can use it to write to later
source_Sv_path = source_Sv
# TODO: In the future we can improve this by obtaining the variable names, channels,
# and dimension lengths directly from source_Sv using zarr or netcdf4. This would
# prevent the unnecessary loading in of the coordinates, which the below statement does.
# open up Dataset using source_Sv path
source_Sv = xr.open_dataset(source_Sv, engine=file_type, chunks={}, **storage_options)
# raise not implemented error if source_Sv corresponds to MVBS
if source_Sv.attrs["processing_function"] == "commongrid.compute_MVBS":
raise NotImplementedError("Adding split-beam data to MVBS has not been implemented!")
# check that the appropriate waveform and encode mode have been given
# and obtain the echodata group path corresponding to encode_mode
ed_beam_group = retrieve_correct_beam_group(echodata, waveform_mode, encode_mode)
# check that source_Sv at least has a channel dimension
if "channel" not in source_Sv.variables:
raise ValueError("The input source_Sv Dataset must have a channel dimension!")
# Select ds_beam channels from source_Sv
ds_beam = echodata[ed_beam_group].sel(channel=source_Sv["channel"].values)
# Assemble angle param dict
angle_param_list = [
"angle_sensitivity_alongship",
"angle_sensitivity_athwartship",
"angle_offset_alongship",
"angle_offset_athwartship",
]
angle_params = {}
for p_name in angle_param_list:
if p_name in source_Sv:
angle_params[p_name] = source_Sv[p_name]
else:
raise ValueError(f"source_Sv does not contain the necessary parameter {p_name}!")
# fail if source_Sv and ds_beam do not have the same lengths
# for ping_time, range_sample, and channel
same_dim_lens = [
ds_beam.dims[dim] == source_Sv.dims[dim] for dim in ["channel", "ping_time", "range_sample"]
]
if not same_dim_lens:
raise ValueError(
"The 'source_Sv' dataset does not have the same dimensions as data in 'echodata'!"
)
# obtain split-beam angles from
# CW mode data
if waveform_mode == "CW":
if encode_mode == "power": # power data
theta, phi = get_angle_power_samples(ds_beam, angle_params)
else: # complex data
# operation is identical with BB complex data
theta, phi = get_angle_complex_samples(ds_beam, angle_params)
# BB mode data
else:
if pulse_compression: # with pulse compression
# put receiver fs into the same dict for simplicity
pc_params = get_filter_coeff(
echodata["Vendor_specific"].sel(channel=source_Sv["channel"].values)
)
pc_params["receiver_sampling_frequency"] = source_Sv["receiver_sampling_frequency"]
theta, phi = get_angle_complex_samples(ds_beam, angle_params, pc_params)
else: # without pulse compression
# operation is identical with CW complex data
theta, phi = get_angle_complex_samples(ds_beam, angle_params)
# add theta and phi to source_Sv input
source_Sv = add_angle_to_ds(
theta, phi, source_Sv, return_dataset, source_Sv_path, file_type, storage_options
)
# Add history attribute
history_attr = (
f"{datetime.datetime.utcnow()} +00:00. "
"Calculated using data stored in the Beam groups of the echodata object." # noqa
)
for da_name in ["angle_alongship", "angle_athwartship"]:
source_Sv[da_name] = source_Sv[da_name].assign_attrs({"history": history_attr})
return source_Sv
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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,846 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/__init__.py | """
Unpack manufacturer-specific data files into an interoperable netCDF or Zarr format.
The current version supports:
- Simrad EK60 echosounder ``.raw`` data
- Simrad EK80 echosounder ``.raw`` data
- ASL Environmental Sciences AZFP echosounder ``.01A`` data
"""
# flake8: noqa
from .parse_ad2cp import ParseAd2cp
from .parse_azfp import ParseAZFP
from .parse_base import ParseBase
from .parse_ek60 import ParseEK60
from .parse_ek80 import ParseEK80
from .set_groups_ad2cp import SetGroupsAd2cp
from .set_groups_azfp import SetGroupsAZFP
from .set_groups_ek60 import SetGroupsEK60
from .set_groups_ek80 import SetGroupsEK80
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"/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,847 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/utils/prov.py | import functools
import re
from datetime import datetime as dt
from pathlib import Path
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import xarray as xr
from _echopype_version import version as ECHOPYPE_VERSION
from numpy.typing import NDArray
from typing_extensions import Literal
from .log import _init_logger
ProcessType = Literal["conversion", "combination", "processing", "mask"]
# Note that this PathHint is defined differently from the one in ..core
PathHint = Union[str, Path]
PathSequenceHint = Union[List[PathHint], Tuple[PathHint], NDArray[PathHint]]
logger = _init_logger(__name__)
def echopype_prov_attrs(process_type: ProcessType) -> Dict[str, str]:
"""
Standard echopype software attributes for provenance
Parameters
----------
process_type : ProcessType
Echopype process function type
"""
prov_dict = {
f"{process_type}_software_name": "echopype",
f"{process_type}_software_version": ECHOPYPE_VERSION,
f"{process_type}_time": dt.utcnow().isoformat(timespec="seconds") + "Z", # use UTC time
}
return prov_dict
def _sanitize_source_files(paths: Union[PathHint, PathSequenceHint]):
"""
Create sanitized list of string paths from heterogeneous path inputs.
Parameters
----------
paths : Union[PathHint, PathSequenceHint]
File paths as either a single path string or pathlib Path,
a sequence (tuple, list or np.ndarray) of strings or pathlib Paths,
or a mixed sequence that may contain another sequence as an element.
Returns
-------
paths_list : List[str]
List of file paths. Empty list if no source path element was parsed successfully.
"""
sequence_types = (list, tuple, np.ndarray)
if isinstance(paths, (str, Path)):
return [str(paths)]
elif isinstance(paths, sequence_types):
paths_list = []
for p in paths:
if isinstance(p, (str, Path)):
paths_list.append(str(p))
elif isinstance(p, sequence_types):
paths_list += [str(pp) for pp in p if isinstance(pp, (str, Path))]
else:
logger.warning(
"Unrecognized file path element type, path element will not be"
f" written to (meta)source_file provenance attribute. {p}"
)
return paths_list
else:
logger.warning(
"Unrecognized file path element type, path element will not be"
f" written to (meta)source_file provenance attribute. {paths}"
)
return []
def source_files_vars(
source_paths: Union[PathHint, PathSequenceHint],
meta_source_paths: Union[PathHint, PathSequenceHint] = None,
) -> Dict[str, Dict[str, Tuple]]:
"""
Create source_filenames and meta_source_filenames provenance
variables dicts to be used for creating xarray DataArray.
Parameters
----------
source_paths : Union[PathHint, PathSequenceHint]
Source file paths as either a single path string or pathlib Path,
a sequence (tuple, list or np.ndarray) of strings or pathlib Paths,
or a mixed sequence that may contain another sequence as an element.
meta_source_paths : Union[PathHint, PathSequenceHint]
Source file paths for metadata files (often as XML files), as either a
single path string or pathlib Path, a sequence (tuple, list or np.ndarray)
of strings or pathlib Paths, or a mixed sequence that may contain another
sequence as an element.
Returns
-------
files_vars : Dict[str, Dict[str, Tuple]]
Contains 3 items:
source_files_var : Dict[str, Tuple]
Single-element dict containing a tuple for creating the
source_filenames xarray DataArray with filenames dimension
meta_source_files_var : Dict[str, Tuple]
Single-element dict containing a tuple for creating the
meta_source_filenames xarray DataArray with filenames dimension
source_files_coord : Dict[str, Tuple]
Single-element dict containing a tuple for creating the
filenames coordinate variable xarray DataArray
"""
source_files = _sanitize_source_files(source_paths)
files_vars = dict()
files_vars["source_files_var"] = {
"source_filenames": (
"filenames",
source_files,
{"long_name": "Source filenames"},
),
}
if meta_source_paths is None or meta_source_paths == "":
files_vars["meta_source_files_var"] = None
else:
meta_source_files = _sanitize_source_files(meta_source_paths)
files_vars["meta_source_files_var"] = {
"meta_source_filenames": (
"filenames",
meta_source_files,
{"long_name": "Metadata source filenames"},
),
}
files_vars["source_files_coord"] = {
"filenames": (
"filenames",
list(range(len(source_files))),
{"long_name": "Index for data and metadata source filenames"},
),
}
return files_vars
def _check_valid_latlon(ds):
"""Verify that the dataset contains valid latitude and longitude variables"""
if (
"longitude" in ds
and not ds["longitude"].isnull().all()
and "latitude" in ds
and not ds["latitude"].isnull().all()
):
return True
else:
return False
# L0 is not actually used by echopype but is included for completeness
PROCESSING_LEVELS = dict(
L0="Level 0",
L1A="Level 1A",
L1B="Level 1B",
L2A="Level 2A",
L2B="Level 2B",
L3A="Level 3A",
L3B="Level 3B",
L4="Level 4",
)
def add_processing_level(processing_level_code: str, is_echodata: bool = False) -> Any:
"""
Wraps functions or methods that return either an xr.Dataset or an echodata object
Parameters
----------
processing_level_code : str
Data processing level code. Can be either the exact code (eg, L1A, L2B, L4)
or using * as a wildcard for either level or sublevel (eg, L*A, L2*) where
the wildcard value of the input is propagated to the output.
is_echodata : bool
Flag specifying if the decorated function returns an EchoData object (optional)
Returns
-------
An xr.Dataset or EchoData object with processing level attributes
inserted if appropriate, or unchanged otherwise.
"""
def wrapper(func):
# TODO: Add conventions attr, with "ACDD-1.3" entry, if not already present?
def _attrs_dict(processing_level):
return {
"processing_level": processing_level,
"processing_level_url": "https://echopype.readthedocs.io/en/stable/processing-levels.html", # noqa
}
if not (
processing_level_code in PROCESSING_LEVELS
or re.fullmatch(r"L\*[A|B]|L[1-4]\*", processing_level_code)
):
raise ValueError(
f"Decorator processing_level_code {processing_level_code} "
f"used in {func.__qualname__} is invalid."
)
# Found the class method vs module function solution in
# https://stackoverflow.com/a/49100736
if len(func.__qualname__.split(".")) > 1:
# Handle class methods
@functools.wraps(func)
def inner(self, *args, **kwargs):
func(self, *args, **kwargs)
if _check_valid_latlon(self["Platform"]):
processing_level = PROCESSING_LEVELS[processing_level_code]
self["Top-level"] = self["Top-level"].assign_attrs(
_attrs_dict(processing_level)
)
else:
logger.info(
"EchoData object (converted raw file) does not contain "
"valid Platform location data. Processing level attributes "
"will not be added."
)
return inner
else:
# Handle stand-alone module functions
@functools.wraps(func)
def inner(*args, **kwargs):
dataobj = func(*args, **kwargs)
if is_echodata:
ed = dataobj
if _check_valid_latlon(ed["Platform"]):
# The decorator is passed the exact, final level code, with sublevel
processing_level = PROCESSING_LEVELS[processing_level_code]
ed["Top-level"] = ed["Top-level"].assign_attrs(
_attrs_dict(processing_level)
)
else:
logger.info(
"EchoData object (converted raw file) does not contain "
"valid Platform location data. Processing level attributes "
"will not be added."
)
return ed
elif isinstance(dataobj, xr.Dataset):
ds = dataobj
if _check_valid_latlon(ds):
if processing_level_code in PROCESSING_LEVELS:
# The decorator is passed the exact, final level code, with sublevel
processing_level = PROCESSING_LEVELS[processing_level_code]
elif (
"*" in processing_level_code
and "input_processing_level" in ds.attrs.keys()
):
if processing_level_code[-1] == "*":
# The decorator is passed a level code without sublevel (eg, L3*).
# The decorated function's "input" dataset's sublevel (A or B) will
# be propagated to the function's output dataset. For L2 and L3
sublevel = ds.attrs["input_processing_level"][-1]
level = processing_level_code[1]
elif processing_level_code[1] == "*":
# The decorator is passed a sublevel code without level (eg, L*A).
# The decorated function's "input" dataset's level (2 or 3) will
# be propagated to the function's output dataset. For L2 and L3
sublevel = processing_level_code[-1]
level = ds.attrs["input_processing_level"][-2]
processing_level = PROCESSING_LEVELS[f"L{level}{sublevel}"]
del ds.attrs["input_processing_level"]
else:
raise RuntimeError(
"Processing level attributes (processing_level_code {processing_level_code}) " # noqa
f"cannot be added. Please ensure that {func.__qualname__} "
"uses the function insert_input_processing_level."
)
ds = ds.assign_attrs(_attrs_dict(processing_level))
else:
logger.info(
"xarray Dataset does not contain valid location data. "
"Processing level attributes will not be added."
)
if "input_processing_level" in ds.attrs:
del ds.attrs["input_processing_level"]
return ds
else:
raise RuntimeError(
f"{func.__qualname__}: Processing level decorator function cannot be used "
"with a function that does not return an xarray Dataset or EchoData object"
)
return inner
return wrapper
def insert_input_processing_level(ds, input_ds):
"""
Copy processing_level attribute from input xr.Dataset, if it exists,
and write it out as input_processing_level
Parameters
----------
ds : xr.Dataset
The xr.Dataset returned by the decorated function
input_ds : xr.Dataset
The xr.Dataset that is the "input" to the decorated function
Returns
-------
ds with input_processing_level attribute inserted if appropriate,
a renamed copy of the processing_level attribute from input_ds if present.
"""
if "processing_level" in input_ds.attrs.keys():
return ds.assign_attrs({"input_processing_level": input_ds.attrs["processing_level"]})
else:
return ds
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"/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,848 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parse_ek80.py | from .parse_base import ParseEK
class ParseEK80(ParseEK):
"""Class for converting data from Simrad EK80 echosounders."""
def __init__(self, file, params, storage_options={}, dgram_zarr_vars={}):
super().__init__(file, params, storage_options, dgram_zarr_vars)
self.environment = {} # dictionary to store environment data
def _select_datagrams(self, params):
"""Translates user input into specific datagrams or ALL"""
def translate_to_dgram(s):
if s == "ALL":
return ["ALL"]
# The GPS flag indicates that only the NME and MRU datagrams are parsed.
# It is kept in the list because it is used in SetGroups
# to flag that only the platform group is saved.
elif s == "GPS":
return ["NME", "MRU"]
# CONFIG flag indicates that only the configuration XML is parsed.
# The XML flag is not needed because the configuration is always
# the first datagram parsed.
elif s == "CONFIG":
return ["CONFIG"]
# XML flag indicates that XML0 datagrams should be read.
# ENV flag indicates that of the XML datagrams,
# only keep the environment datagrams
elif s == "ENV":
return ["XML", "ENV"]
# EXPORT_XML flag passed in only by the to_xml function
# Used to print the export message when writing to an xml file
elif s == "EXPORT_XML":
return ["print_export_msg"]
else:
raise ValueError("Unknown data type", params)
# Params is a string when user sets data_type in to_netcdf/to_zarr
if isinstance(params, str):
dgrams = translate_to_dgram(params)
# Params is a list when the parse classes are called by to_xml
else:
dgrams = []
for p in params:
dgrams += translate_to_dgram(p)
return dgrams
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,849 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/echodata/test_echodata_simrad.py | """
Tests functions contained within echodata/simrad.py
"""
import pytest
from echopype.echodata.simrad import retrieve_correct_beam_group, check_input_args_combination
@pytest.mark.parametrize(
("waveform_mode", "encode_mode", "pulse_compression"),
[
pytest.param("CW", "comp_power", None,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since comp_power '
'is not an acceptable choice for encode_mode.')),
pytest.param("CB", None, None,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since CB is not an '
'acceptable choice for waveform_mode.')),
pytest.param("BB", "power", None,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since BB and power is '
'not an acceptable combination.')),
pytest.param("BB", "power", True,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since BB and complex '
'must be used if pulse_compression is True.')),
pytest.param("CW", "complex", True,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since BB and complex '
'must be used if pulse_compression is True.')),
pytest.param("CW", "power", True,
marks=pytest.mark.xfail(strict=True,
reason='This test should fail since BB and complex '
'must be used if pulse_compression is True.')),
("CW", "complex", False),
("CW", "power", False),
("BB", "complex", False),
("BB", "complex", True),
],
ids=["incorrect_encode_mode", "incorrect_waveform_mode", "BB_power_combo",
"BB_power_pc_True", "CW_complex_pc_True", "CW_power_pc_True", "CW_complex_pc_False",
"CW_power_pc_False", "BB_complex_pc_False", "BB_complex_pc_True"]
)
def test_check_input_args_combination(waveform_mode: str, encode_mode: str,
pulse_compression: bool):
"""
Ensures that ``check_input_args_combination`` functions correctly when
provided various combinations of the input parameters.
Parameters
----------
waveform_mode: str
Type of transmit waveform
encode_mode: str
Type of encoded return echo data
pulse_compression: bool
States whether pulse compression should be used
"""
check_input_args_combination(waveform_mode, encode_mode, pulse_compression)
def test_retrieve_correct_beam_group():
# TODO: create this test once we are happy with the form of retrieve_correct_beam_group
pytest.skip("We need to add tests for retrieve_correct_beam_group!")
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,850 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/utils/test_coding.py | import pytest
import numpy as np
import xarray as xr
import math
import dask
from echopype.utils.coding import _get_auto_chunk, set_netcdf_encodings
@pytest.mark.parametrize(
"chunk",
["auto", "5MB", "10MB", "30MB", "70MB", "100MB", "default"],
)
def test__get_auto_chunk(chunk):
random_data = 15 + 8 * np.random.randn(10, 1000, 1000)
da = xr.DataArray(
data=random_data,
dims=["x", "y", "z"]
)
if chunk == "auto":
dask_data = da.chunk('auto').data
elif chunk == "default":
dask_data = da.chunk(_get_auto_chunk(da)).data
else:
dask_data = da.chunk(_get_auto_chunk(da, chunk)).data
chunk_byte_size = math.prod(dask_data.chunksize + (dask_data.itemsize,))
if chunk in ["auto", "100MB", "default"]:
assert chunk_byte_size == dask_data.nbytes, "Default chunk is not equal to data array size!"
else:
assert chunk_byte_size <= dask.utils.parse_bytes(chunk), "Calculated chunk exceeded max chunk!"
def test_set_netcdf_encodings():
# create a test dataset
ds = xr.Dataset(
{
"var1": xr.DataArray(np.random.rand(10), dims="dim1"),
"var2": xr.DataArray(np.random.rand(10), dims="dim1", attrs={"attr1": "value1"}),
"var3": xr.DataArray(["a", "b", "c"], dims="dim2"),
},
attrs={"global_attr": "global_value"},
)
# test with default compression settings
encoding = set_netcdf_encodings(ds, {})
assert isinstance(encoding, dict)
assert len(encoding) == 3
assert "var1" in encoding
assert "var2" in encoding
assert "var3" in encoding
assert encoding["var1"]["zlib"] is True
assert encoding["var1"]["complevel"] == 4
assert encoding["var2"]["zlib"] is True
assert encoding["var2"]["complevel"] == 4
assert encoding["var3"]["zlib"] is False
# test with custom compression settings
compression_settings = {"zlib": True, "complevel": 5}
encoding = set_netcdf_encodings(ds, compression_settings)
assert isinstance(encoding, dict)
assert len(encoding) == 3
assert "var1" in encoding
assert "var2" in encoding
assert "var3" in encoding
assert encoding["var1"]["zlib"] is True
assert encoding["var1"]["complevel"] == 5
assert encoding["var2"]["zlib"] is True
assert encoding["var2"]["complevel"] == 5
assert encoding["var3"]["zlib"] is False
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,851 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/convention/conv.py | from importlib import resources
from typing import Optional
import yaml
from .. import convention
class _Convention:
def __init__(self, version: Optional[str]):
"""Prepare to read the convention yaml file"""
self._yaml_dict = {}
# Hardwired to 1.0, for now
self.version = "1.0"
if version:
self.version = version
@property
def yaml_dict(self):
"""Read data from disk"""
if self._yaml_dict: # Data has already been read, return it directly
return self._yaml_dict
with resources.open_text(package=convention, resource=f"{self.version}.yml") as fid:
convention_yaml = yaml.load(fid, Loader=yaml.SafeLoader)
self._yaml_dict = convention_yaml
return self._yaml_dict
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,852 | OSOceanAcoustics/echopype | refs/heads/main | /.ci_helpers/run-test.py | """run-test.py
Script to run tests in Github and locally.
"""
import argparse
import glob
import os
import re
import shutil
import sys
from pathlib import Path
import pytest
from pytest import ExitCode
EXIT_CODES = {
ExitCode.OK: 0,
ExitCode.TESTS_FAILED: 1,
ExitCode.INTERRUPTED: 2,
ExitCode.INTERNAL_ERROR: 3,
ExitCode.USAGE_ERROR: 4,
ExitCode.NO_TESTS_COLLECTED: 5,
}
MODULES_TO_TEST = {
"root": {}, # This is to test the root folder.
"calibrate": {},
"clean": {},
"commongrid": {},
"consolidate": {},
"convert": {},
"echodata": {},
"mask": {},
"metrics": {},
"qc": {},
"utils": {},
"visualize": {},
}
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run tests listed.")
parser.add_argument(
"touchedfiles",
metavar="TOUCHED_FILES",
type=str,
nargs="?",
default="",
help="Comma separated list of changed files.",
)
parser.add_argument("--pytest-args", type=str, help="Optional pytest args", default="")
parser.add_argument(
"--local",
action="store_true",
help="Optional flag for running tests locally, not in continuous integration.",
)
parser.add_argument(
"--include-cov",
action="store_true",
help="Optional flag for including coverage. Exports to coverage.xml by default.",
)
args = parser.parse_args()
if args.local:
temp_path = Path("~/.echopype/temp_output")
dump_path = Path("echopype/test_data/dump")
if temp_path.exists():
shutil.rmtree(temp_path)
if dump_path.exists():
shutil.rmtree(dump_path)
if args.touchedfiles == "":
echopype_folder = Path("echopype")
file_list = glob.glob(str(echopype_folder / "**" / "*.py"), recursive=True)
else:
file_list = args.touchedfiles.split(",")
else:
file_list = args.touchedfiles.split(",")
pytest_args = []
if args.pytest_args:
pytest_args = args.pytest_args.split(",")
if args.include_cov:
# Checks for cov in pytest_args
for arg in pytest_args:
if re.match("--cov", arg) is not None:
raise ValueError(
"pytest args may not have any cov arguments if --include-cov is set."
)
pytest_args = pytest_args + [
"--cov-report=xml",
"--cov-append",
]
test_to_run = {}
for module, mod_extras in MODULES_TO_TEST.items():
if module == "root":
file_globs = [
"echopype/*",
"echopype/tests/*",
]
else:
file_globs = [
f"echopype/{module}/*",
f"echopype/tests/{module}/*",
]
if "extra_globs" in mod_extras:
file_globs = file_globs + mod_extras["extra_globs"]
for f in file_list:
file_path = Path(f)
file_name, file_ext = os.path.splitext(os.path.basename(f))
if file_ext == ".py":
if any(((file_path.match(fg)) for fg in file_globs)):
if module not in test_to_run:
test_to_run[module] = []
test_to_run[module].append(file_path)
original_pytest_args = pytest_args.copy()
total_exit_codes = []
for k, v in test_to_run.items():
print(f"=== RUNNING {k.upper()} TESTS===")
print(f"Touched files: {','.join([os.path.basename(p) for p in v])}")
if k == "root":
file_glob_str = "echopype/tests/test_*.py"
cov_mod_arg = ["--cov=echopype"]
else:
file_glob_str = f"echopype/tests/{k}/*.py"
cov_mod_arg = [f"--cov=echopype/{k}"]
if args.include_cov:
pytest_args = original_pytest_args + cov_mod_arg
test_files = glob.glob(file_glob_str)
final_args = pytest_args + test_files
print(f"Pytest args: {final_args}")
exit_code = pytest.main(final_args)
total_exit_codes.append(EXIT_CODES[exit_code])
if len(total_exit_codes) == 0:
print("No test(s) were run.")
sys.exit(0)
if all(True if e == 0 else False for e in total_exit_codes):
print("All tests have been run successfully!")
sys.exit(0)
else:
print("Some runs have failed. Please see the log.")
sys.exit(1)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,853 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/metrics/__init__.py | """
Functions to compute summary statistics from echo data.
"""
from .summary_statistics import abundance, aggregation, center_of_mass, dispersion, evenness
__all__ = [
"abundance",
"aggregation",
"center_of_mass",
"dispersion",
"evenness",
]
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,854 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/calibrate/test_calibrate_ek80.py | import pytest
import numpy as np
import pandas as pd
import xarray as xr
import echopype as ep
@pytest.fixture
def ek80_path(test_path):
return test_path['EK80']
@pytest.fixture
def ek80_cal_path(test_path):
return test_path['EK80_CAL']
@pytest.fixture
def ek80_ext_path(test_path):
return test_path['EK80_EXT']
def test_ek80_transmit_chirp(ek80_cal_path, ek80_ext_path):
"""
Test transmit chirp reconstruction against Andersen et al. 2021/pyEcholab implementation
"""
ek80_raw_path = ek80_cal_path / "2018115-D20181213-T094600.raw" # rx impedance / rx fs / tcvr type
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
# Calibration object detail
waveform_mode = "BB"
encode_mode = "complex"
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode=waveform_mode, encode_mode=encode_mode,
env_params=None, cal_params=None
)
fs = cal_obj.cal_params["receiver_sampling_frequency"]
filter_coeff = ep.calibrate.ek80_complex.get_filter_coeff(ed["Vendor_specific"].sel(channel=cal_obj.chan_sel))
tx, tx_time = ep.calibrate.ek80_complex.get_transmit_signal(
ed["Sonar/Beam_group1"].sel(channel=cal_obj.chan_sel), filter_coeff, waveform_mode, fs
)
tau_effective = ep.calibrate.ek80_complex.get_tau_effective(
ytx_dict=tx,
fs_deci_dict={k: 1 / np.diff(v[:2]) for (k, v) in tx_time.items()}, # decimated fs
waveform_mode=cal_obj.waveform_mode,
channel=cal_obj.chan_sel,
ping_time=cal_obj.echodata["Sonar/Beam_group1"]["ping_time"],
)
# Load pyEcholab object: channel WBT 714590-15 ES70-7C
import pickle
with open(ek80_ext_path / "pyecholab/pyel_BB_calibration.pickle", 'rb') as handle:
pyecholab_BB = pickle.load(handle)
# Compare first ping since all params identical
ch_sel = "WBT 714590-15 ES70-7C"
# receive sampling frequency
assert pyecholab_BB["rx_sample_frequency"][0] == fs.sel(channel=ch_sel)
# WBT filter
assert np.all(pyecholab_BB["filters"][1]["coefficients"] == filter_coeff[ch_sel]["wbt_fil"])
assert np.all(pyecholab_BB["filters"][1]["decimation_factor"] == filter_coeff[ch_sel]["wbt_decifac"])
# PC filter
assert np.all(pyecholab_BB["filters"][2]["coefficients"] == filter_coeff[ch_sel]["pc_fil"])
assert np.all(pyecholab_BB["filters"][2]["decimation_factor"] == filter_coeff[ch_sel]["pc_decifac"])
# transmit signal
assert np.allclose(pyecholab_BB["_tx_signal"][0], tx[ch_sel])
# tau effective
# use np.isclose for now since difference is 2.997176e-5 (pyecholab) and 2.99717595e-05 (echopype)
# will see if it causes downstream major differences
assert np.isclose(tau_effective.sel(channel=ch_sel).data, pyecholab_BB["_tau_eff"][0])
def test_ek80_BB_params(ek80_cal_path, ek80_ext_path):
"""
Test power from pulse compressed BB data
"""
ek80_raw_path = ek80_cal_path / "2018115-D20181213-T094600.raw" # rx impedance / rx fs / tcvr type
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
# Calibration object detail
waveform_mode = "BB"
encode_mode = "complex"
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode=waveform_mode, encode_mode=encode_mode,
env_params={"formula_absorption": "FG"}, cal_params=None
)
z_er = cal_obj.cal_params["impedance_transceiver"]
z_et = cal_obj.cal_params["impedance_transducer"]
# B_theta_phi_m = cal_obj._get_B_theta_phi_m()
params_BB_map = {
# param name mapping: echopype (ep) : pyecholab (pyel)
"angle_offset_alongship": "angle_offset_alongship",
"angle_offset_athwartship": "angle_offset_athwartship",
"beamwidth_alongship": "beam_width_alongship",
"beamwidth_athwartship": "beam_width_athwartship",
"gain_correction": "gain", # this is *before* B_theta_phi_m BB correction
}
# Load pyEcholab object: channel WBT 714590-15 ES70-7C
import pickle
with open(ek80_ext_path / "pyecholab/pyel_BB_calibration.pickle", 'rb') as handle:
pyel_BB_cal = pickle.load(handle)
with open(ek80_ext_path / "pyecholab/pyel_BB_raw_data.pickle", 'rb') as handle:
pyel_BB_raw = pickle.load(handle)
ch_sel = "WBT 714590-15 ES70-7C"
# pyecholab calibration object
# TODO: need to check B_theta_phi_m values
assert pyel_BB_cal["impedance"] == z_er.sel(channel=ch_sel)
for p_ep, p_pyel in params_BB_map.items(): # all interpolated BB params
assert np.isclose(pyel_BB_cal[p_pyel][0], cal_obj.cal_params[p_ep].sel(channel=ch_sel).isel(ping_time=0))
assert pyel_BB_cal["sa_correction"][0] == cal_obj.cal_params["sa_correction"].sel(channel=ch_sel).isel(ping_time=0)
assert pyel_BB_cal["sound_speed"] == cal_obj.env_params["sound_speed"]
assert np.isclose(
pyel_BB_cal["absorption_coefficient"][0],
cal_obj.env_params["sound_absorption"].sel(channel=ch_sel).isel(ping_time=0)
)
# pyecholab raw_data object
assert pyel_BB_raw["ZTRANSDUCER"] == z_et.sel(channel=ch_sel).isel(ping_time=0)
assert pyel_BB_raw["transmit_power"][0] == ed["Sonar/Beam_group1"]["transmit_power"].sel(channel=ch_sel).isel(ping_time=0)
assert pyel_BB_raw["transceiver_type"] == ed["Vendor_specific"]["transceiver_type"].sel(channel=ch_sel)
def test_ek80_BB_range(ek80_cal_path, ek80_ext_path):
ek80_raw_path = ek80_cal_path / "2018115-D20181213-T094600.raw" # rx impedance / rx fs / tcvr type
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
# Calibration object
waveform_mode = "BB"
encode_mode = "complex"
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode=waveform_mode, encode_mode=encode_mode,
env_params={"formula_absorption": "FG"}, cal_params=None
)
ch_sel = "WBT 714590-15 ES70-7C"
# Load pyecholab pickle
import pickle
with open(ek80_ext_path / "pyecholab/pyel_BB_p_data.pickle", 'rb') as handle:
pyel_BB_p_data = pickle.load(handle)
# Assert
ep_vals = cal_obj.range_meter.sel(channel=ch_sel).isel(ping_time=0).data
pyel_vals = pyel_BB_p_data["range"]
assert np.allclose(pyel_vals, ep_vals)
def test_ek80_BB_power_Sv(ek80_cal_path, ek80_ext_path):
ek80_raw_path = ek80_cal_path / "2018115-D20181213-T094600.raw" # rx impedance / rx fs / tcvr type
ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
# Calibration object
waveform_mode = "BB"
encode_mode = "complex"
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata=ed, waveform_mode=waveform_mode, encode_mode=encode_mode,
env_params={"formula_absorption": "FG"}, cal_params=None
)
# Params needed
beam = cal_obj.echodata[cal_obj.ed_beam_group].sel(channel=cal_obj.chan_sel)
z_er = cal_obj.cal_params["impedance_transceiver"]
z_et = cal_obj.cal_params["impedance_transducer"]
fs = cal_obj.cal_params["receiver_sampling_frequency"]
filter_coeff = ep.calibrate.ek80_complex.get_filter_coeff(ed["Vendor_specific"].sel(channel=cal_obj.chan_sel))
tx, tx_time = ep.calibrate.ek80_complex.get_transmit_signal(beam, filter_coeff, waveform_mode, fs)
# Get power from complex samples
prx = cal_obj._get_power_from_complex(beam=beam, chirp=tx, z_et=z_et, z_er=z_er)
ch_sel = "WBT 714590-15 ES70-7C"
# Load pyecholab pickle
import pickle
with open(ek80_ext_path / "pyecholab/pyel_BB_p_data.pickle", 'rb') as handle:
pyel_BB_p_data = pickle.load(handle)
# Power: only compare non-Nan, non-Inf values
pyel_vals = pyel_BB_p_data["power"]
ep_vals = 10 * np.log10(prx.sel(channel=ch_sel).data)
assert pyel_vals.shape == ep_vals.shape
idx_to_cmp = ~(
np.isinf(pyel_vals) | np.isnan(pyel_vals) | np.isinf(ep_vals) | np.isnan(ep_vals)
)
assert np.allclose(pyel_vals[idx_to_cmp], ep_vals[idx_to_cmp])
# Sv: only compare non-Nan, non-Inf values
# comparing for only the last values now until fixing the range computation
ds_Sv = ep.calibrate.compute_Sv(
ed, waveform_mode="BB", encode_mode="complex"
)
pyel_vals = pyel_BB_p_data["sv_data"]
ep_vals = ds_Sv["Sv"].sel(channel=ch_sel).squeeze().data
assert pyel_vals.shape == ep_vals.shape
idx_to_cmp = ~(
np.isinf(pyel_vals) | np.isnan(pyel_vals) | np.isinf(ep_vals) | np.isnan(ep_vals)
)
assert np.allclose(pyel_vals[idx_to_cmp], ep_vals[idx_to_cmp])
def test_ek80_BB_power_echoview(ek80_path):
"""Compare pulse compressed outputs from echopype and csv exported from EchoView.
Unresolved: the difference is large and it is not clear why.
"""
ek80_raw_path = str(ek80_path.joinpath('D20170912-T234910.raw'))
ek80_bb_pc_test_path = str(
ek80_path.joinpath(
'from_echoview', '70 kHz pulse-compressed power.complex.csv'
)
)
echodata = ep.open_raw(ek80_raw_path, sonar_model='EK80')
# Create a CalibrateEK80 object to perform pulse compression
cal_obj = ep.calibrate.calibrate_ek.CalibrateEK80(
echodata, env_params=None, cal_params=None, waveform_mode="BB", encode_mode="complex"
)
beam = echodata["Sonar/Beam_group1"].sel(channel=cal_obj.chan_sel)
coeff = ep.calibrate.ek80_complex.get_filter_coeff(echodata["Vendor_specific"].sel(channel=cal_obj.chan_sel))
chirp, _ = ep.calibrate.ek80_complex.get_transmit_signal(beam, coeff, "BB", cal_obj.cal_params["receiver_sampling_frequency"])
pc = ep.calibrate.ek80_complex.compress_pulse(
backscatter=beam["backscatter_r"] + 1j * beam["backscatter_i"], chirp=chirp)
pc = pc / ep.calibrate.ek80_complex.get_norm_fac(chirp) # normalization for each channel
pc_mean = pc.sel(channel="WBT 549762-15 ES70-7C").mean(dim="beam").dropna("range_sample")
# Read EchoView pc raw power output
df = pd.read_csv(ek80_bb_pc_test_path, header=None, skiprows=[0])
df_header = pd.read_csv(
ek80_bb_pc_test_path, header=0, usecols=range(14), nrows=0
)
df = df.rename(
columns={
cc: vv for cc, vv in zip(df.columns, df_header.columns.values)
}
)
df.columns = df.columns.str.strip()
df_real = df.loc[df["Component"] == " Real", :].iloc[:, 14:] # values start at column 15
# Skip an initial chunk of samples due to unknown larger difference
# this difference is also documented in pyecholab tests
# Below only compare the first ping
ev_vals = df_real.values[:, :]
ep_vals = pc_mean.values.real[:, :]
assert np.allclose(ev_vals[:, 69:8284], ep_vals[:, 69:], atol=1e-4)
assert np.allclose(ev_vals[:, 90:8284], ep_vals[:, 90:], atol=1e-5) | {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,855 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/commongrid/test_nasc.py | import pytest
import numpy as np
from echopype import open_raw
from echopype.calibrate import compute_Sv
from echopype.commongrid import compute_NASC
from echopype.commongrid.nasc import (
get_distance_from_latlon,
get_depth_bin_info,
get_dist_bin_info,
get_distance_from_latlon,
)
from echopype.consolidate import add_location, add_depth
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
# def test_compute_NASC(ek60_path):
# raw_path = ek60_path / "ncei-wcsd/Summer2017-D20170620-T011027.raw"
# ed = open_raw(raw_path, sonar_model="EK60")
# ds_Sv = add_depth(add_location(compute_Sv(ed), ed, nmea_sentence="GGA"))
# cell_dist = 0.1
# cell_depth = 20
# ds_NASC = compute_NASC(ds_Sv, cell_dist, cell_depth)
# dist_nmi = get_distance_from_latlon(ds_Sv)
# # Check dimensions
# da_NASC = ds_NASC["NASC"]
# assert da_NASC.dims == ("channel", "distance", "depth")
# assert np.all(ds_NASC["channel"].values == ds_Sv["channel"].values)
# assert da_NASC["depth"].size == np.ceil(ds_Sv["depth"].max() / cell_depth)
# assert da_NASC["distance"].size == np.ceil(dist_nmi.max() / cell_dist)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,856 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/api.py | from pathlib import Path
from typing import TYPE_CHECKING, Dict, Optional, Tuple
import fsspec
from datatree import DataTree
# fmt: off
# black and isort have conflicting ideas about how this should be formatted
from ..core import SONAR_MODELS
from .parsed_to_zarr import Parsed2Zarr
if TYPE_CHECKING:
from ..core import EngineHint, PathHint, SonarModelsHint
# fmt: on
from ..echodata.echodata import XARRAY_ENGINE_MAP, EchoData
from ..utils import io
from ..utils.coding import COMPRESSION_SETTINGS
from ..utils.log import _init_logger
from ..utils.prov import add_processing_level
BEAM_SUBGROUP_DEFAULT = "Beam_group1"
# Logging setup
logger = _init_logger(__name__)
def to_file(
echodata: EchoData,
engine: "EngineHint",
save_path: Optional["PathHint"] = None,
compress: bool = True,
overwrite: bool = False,
parallel: bool = False,
output_storage_options: Dict[str, str] = {},
**kwargs,
):
"""Save content of EchoData to netCDF or zarr.
Parameters
----------
engine : str {'netcdf4', 'zarr'}
type of converted file
save_path : str
path that converted .nc file will be saved
compress : bool
whether or not to perform compression on data variables
Defaults to ``True``
overwrite : bool
whether or not to overwrite existing files
Defaults to ``False``
parallel : bool
whether or not to use parallel processing. (Not yet implemented)
output_storage_options : dict
Additional keywords to pass to the filesystem class.
**kwargs : dict, optional
Extra arguments to either `xr.Dataset.to_netcdf`
or `xr.Dataset.to_zarr`: refer to each method documentation
for a list of all possible arguments.
"""
if parallel:
raise NotImplementedError("Parallel conversion is not yet implemented.")
if engine not in XARRAY_ENGINE_MAP.values():
raise ValueError("Unknown type to convert file to!")
# Assemble output file names and path
output_file = io.validate_output_path(
source_file=echodata.source_file,
engine=engine,
save_path=save_path,
output_storage_options=output_storage_options,
)
# Get all existing files
fs = fsspec.get_mapper(output_file, **output_storage_options).fs # get file system
exists = True if fs.exists(output_file) else False
# Sequential or parallel conversion
if exists and not overwrite:
logger.info(
f"{echodata.source_file} has already been converted to {engine}. " # noqa
f"File saving not executed."
)
else:
if exists:
logger.info(f"overwriting {output_file}")
else:
logger.info(f"saving {output_file}")
_save_groups_to_file(
echodata,
output_path=io.sanitize_file_path(
file_path=output_file, storage_options=output_storage_options
),
engine=engine,
compress=compress,
**kwargs,
)
# Link path to saved file with attribute as if from open_converted
echodata.converted_raw_path = output_file
def _save_groups_to_file(echodata, output_path, engine, compress=True, **kwargs):
"""Serialize all groups to file."""
# TODO: in terms of chunking, would using rechunker at the end be faster and more convenient?
# TODO: investigate chunking before we save Dataset to a file
# Top-level group
io.save_file(
echodata["Top-level"],
path=output_path,
mode="w",
engine=engine,
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Environment group
io.save_file(
echodata["Environment"], # TODO: chunking necessary?
path=output_path,
mode="a",
engine=engine,
group="Environment",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Platform group
io.save_file(
echodata["Platform"], # TODO: chunking necessary? time1 and time2 (EK80) only
path=output_path,
mode="a",
engine=engine,
group="Platform",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Platform/NMEA group: some sonar model does not produce NMEA data
if echodata["Platform/NMEA"] is not None:
io.save_file(
echodata["Platform/NMEA"], # TODO: chunking necessary?
path=output_path,
mode="a",
engine=engine,
group="Platform/NMEA",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Provenance group
io.save_file(
echodata["Provenance"],
path=output_path,
group="Provenance",
mode="a",
engine=engine,
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Sonar group
io.save_file(
echodata["Sonar"],
path=output_path,
group="Sonar",
mode="a",
engine=engine,
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# /Sonar/Beam_groupX group
if echodata.sonar_model == "AD2CP":
for i in range(1, len(echodata["Sonar"]["beam_group"]) + 1):
io.save_file(
echodata[f"Sonar/Beam_group{i}"],
path=output_path,
mode="a",
engine=engine,
group=f"Sonar/Beam_group{i}",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
else:
io.save_file(
echodata[f"Sonar/{BEAM_SUBGROUP_DEFAULT}"],
path=output_path,
mode="a",
engine=engine,
group=f"Sonar/{BEAM_SUBGROUP_DEFAULT}",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
if echodata["Sonar/Beam_group2"] is not None:
# some sonar model does not produce Sonar/Beam_group2
io.save_file(
echodata["Sonar/Beam_group2"],
path=output_path,
mode="a",
engine=engine,
group="Sonar/Beam_group2",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
# Vendor_specific group
io.save_file(
echodata["Vendor_specific"], # TODO: chunking necessary?
path=output_path,
mode="a",
engine=engine,
group="Vendor_specific",
compression_settings=COMPRESSION_SETTINGS[engine] if compress else None,
**kwargs,
)
def _set_convert_params(param_dict: Dict[str, str]) -> Dict[str, str]:
"""Set parameters (metadata) that may not exist in the raw files.
The default set of parameters include:
- Platform group: ``platform_name``, ``platform_type``, ``platform_code_ICES``, ``water_level``
- Top-level group: ``survey_name``
Other parameters will be saved to the top level.
# TODO: revise docstring, give examples.
Examples
--------
# set parameters that may not already be in source files
echodata.set_param({
'platform_name': 'OOI',
'platform_type': 'mooring'
})
"""
out_params = dict()
# Parameters for the Platform group
out_params["platform_name"] = param_dict.get("platform_name", "")
out_params["platform_code_ICES"] = param_dict.get("platform_code_ICES", "")
out_params["platform_type"] = param_dict.get("platform_type", "")
out_params["water_level"] = param_dict.get("water_level", None)
# Parameters for the Top-level group
out_params["survey_name"] = param_dict.get("survey_name", "")
for k, v in param_dict.items():
if k not in out_params:
out_params[k] = v
return out_params
def _check_file(
raw_file,
sonar_model: "SonarModelsHint",
xml_path: Optional["PathHint"] = None,
storage_options: Dict[str, str] = {},
) -> Tuple[str, str]:
"""Checks whether the file and/or xml file exists and
whether they have the correct extensions.
Parameters
----------
raw_file : str
path to raw data file
sonar_model : str
model of the sonar instrument
xml_path : str
path to XML config file used by AZFP
storage_options : dict
options for cloud storage
Returns
-------
file : str
path to existing raw data file
xml : str
path to existing xml file
empty string if no xml file is required for the specified model
"""
if SONAR_MODELS[sonar_model]["xml"]: # if this sonar model expects an XML file
if not xml_path:
raise ValueError(f"XML file is required for {sonar_model} raw data")
else:
if ".XML" not in Path(xml_path).suffix.upper():
raise ValueError(f"{Path(xml_path).name} is not an XML file")
xmlmap = fsspec.get_mapper(str(xml_path), **storage_options)
if not xmlmap.fs.exists(xmlmap.root):
raise FileNotFoundError(f"There is no file named {Path(xml_path).name}")
xml = xml_path
else:
xml = ""
# TODO: https://github.com/OSOceanAcoustics/echopype/issues/229
# to add compatibility for pathlib.Path objects for local paths
fsmap = fsspec.get_mapper(raw_file, **storage_options)
validate_ext = SONAR_MODELS[sonar_model]["validate_ext"]
if not fsmap.fs.exists(fsmap.root):
raise FileNotFoundError(f"There is no file named {Path(raw_file).name}")
validate_ext(Path(raw_file).suffix.upper())
return str(raw_file), str(xml)
@add_processing_level("L1A", is_echodata=True)
def open_raw(
raw_file: "PathHint",
sonar_model: "SonarModelsHint",
xml_path: Optional["PathHint"] = None,
convert_params: Optional[Dict[str, str]] = None,
storage_options: Optional[Dict[str, str]] = None,
use_swap: bool = False,
max_mb: int = 100,
) -> Optional[EchoData]:
"""Create an EchoData object containing parsed data from a single raw data file.
The EchoData object can be used for adding metadata and ancillary data
as well as to serialize the parsed data to zarr or netcdf.
Parameters
----------
raw_file : str
path to raw data file
sonar_model : str
model of the sonar instrument
- ``EK60``: Kongsberg Simrad EK60 echosounder
- ``ES70``: Kongsberg Simrad ES70 echosounder
- ``EK80``: Kongsberg Simrad EK80 echosounder
- ``EA640``: Kongsberg EA640 echosounder
- ``AZFP``: ASL Environmental Sciences AZFP echosounder
- ``AD2CP``: Nortek Signature series ADCP
(tested with Signature 500 and Signature 1000)
xml_path : str
path to XML config file used by AZFP
convert_params : dict
parameters (metadata) that may not exist in the raw file
and need to be added to the converted file
storage_options : dict
options for cloud storage
use_swap: bool
If True, variables with a large memory footprint will be
written to a temporary zarr store at ``~/.echopype/temp_output/parsed2zarr_temp_files``
max_mb : int
The maximum data chunk size in Megabytes (MB), when offloading
variables with a large memory footprint to a temporary zarr store
Returns
-------
EchoData object
Raises
------
ValueError
If ``sonar_model`` is ``None`` or ``sonar_model``
given is unsupported.
FileNotFoundError
If ``raw_file`` is ``None``.
TypeError
If ``raw_file`` input is neither ``str`` or
``pathlib.Path`` type.
Notes
-----
``use_swap=True`` is only available for the following
echosounders: EK60, ES70, EK80, ES80, EA640. Additionally, this feature
is currently in beta.
"""
if raw_file is None:
raise FileNotFoundError("The path to the raw data file must be specified.")
# Check for path type
if isinstance(raw_file, Path):
raw_file = str(raw_file)
if not isinstance(raw_file, str):
raise TypeError("File path must be a string or Path")
if sonar_model is None:
raise ValueError("Sonar model must be specified.")
# Check inputs
if convert_params is None:
convert_params = {}
storage_options = storage_options if storage_options is not None else {}
# Uppercased model in case people use lowercase
sonar_model = sonar_model.upper() # type: ignore
# Check models
if sonar_model not in SONAR_MODELS:
raise ValueError(
f"Unsupported echosounder model: {sonar_model}\nMust be one of: {list(SONAR_MODELS)}" # noqa
)
# Check file extension and existence
file_chk, xml_chk = _check_file(raw_file, sonar_model, xml_path, storage_options)
# TODO: remove once 'auto' option is added
if not isinstance(use_swap, bool):
raise ValueError("use_swap must be of type bool.")
# Ensure use_swap is 'auto', if it is a string
# TODO: use the following when we allow for 'auto' option
# if isinstance(use_swap, str) and use_swap != "auto":
# raise ValueError("use_swap must be a bool or equal to 'auto'.")
# TODO: the if-else below only works for the AZFP vs EK contrast,
# but is brittle since it is abusing params by using it implicitly
if SONAR_MODELS[sonar_model]["xml"]:
params = xml_chk
else:
params = "ALL" # reserved to control if only wants to parse a certain type of datagram
# obtain dict associated with directly writing to zarr
dgram_zarr_vars = SONAR_MODELS[sonar_model]["dgram_zarr_vars"]
# Parse raw file and organize data into groups
parser = SONAR_MODELS[sonar_model]["parser"](
file_chk, params=params, storage_options=storage_options, dgram_zarr_vars=dgram_zarr_vars
)
parser.parse_raw()
# Direct offload to zarr and rectangularization only available for some sonar models
if sonar_model in ["EK60", "ES70", "EK80", "ES80", "EA640"]:
# Create sonar_model-specific p2z object
p2z = SONAR_MODELS[sonar_model]["parsed2zarr"](parser)
# Determines if writing to zarr is necessary and writes to zarr
p2z_flag = use_swap is True or (
use_swap == "auto" and p2z.whether_write_to_zarr(mem_mult=0.4)
)
if p2z_flag:
p2z.datagram_to_zarr(max_mb=max_mb)
# Rectangularize the transmit data
parser.rectangularize_transmit_ping_data(data_type="complex")
else:
del p2z
# Create general p2z object
p2z = Parsed2Zarr(parser)
parser.rectangularize_data()
else:
# No rectangularization for other sonar models
p2z = Parsed2Zarr(parser) # Create general p2z object
setgrouper = SONAR_MODELS[sonar_model]["set_groups"](
parser,
input_file=file_chk,
xml_path=xml_chk,
output_path=None,
sonar_model=sonar_model,
params=_set_convert_params(convert_params),
parsed2zarr_obj=p2z,
)
# Setup tree dictionary
tree_dict = {}
# Top-level date_created varies depending on sonar model
# Top-level is called "root" within tree
if sonar_model in ["EK60", "ES70", "EK80", "ES80", "EA640"]:
tree_dict["/"] = setgrouper.set_toplevel(
sonar_model=sonar_model,
date_created=parser.config_datagram["timestamp"],
)
else:
tree_dict["/"] = setgrouper.set_toplevel(
sonar_model=sonar_model, date_created=parser.ping_time[0]
)
tree_dict["Environment"] = setgrouper.set_env()
tree_dict["Platform"] = setgrouper.set_platform()
if sonar_model in ["EK60", "ES70", "EK80", "ES80", "EA640"]:
tree_dict["Platform/NMEA"] = setgrouper.set_nmea()
tree_dict["Provenance"] = setgrouper.set_provenance()
# Allocate a tree_dict entry for Sonar? Otherwise, a DataTree error occurs
tree_dict["Sonar"] = None
# Set multi beam groups
beam_groups = setgrouper.set_beam()
beam_group_type = []
for idx, beam_group in enumerate(beam_groups, start=1):
if beam_group is not None:
# fill in beam_group_type (only necessary for EK80, ES80, EA640)
if idx == 1:
# choose the appropriate description key for Beam_group1
beam_group_type.append("complex" if "backscatter_i" in beam_group else "power")
else:
# provide None for all other beam groups (since the description does not have a key)
beam_group_type.append(None)
tree_dict[f"Sonar/Beam_group{idx}"] = beam_group
if sonar_model in ["EK80", "ES80", "EA640"]:
tree_dict["Sonar"] = setgrouper.set_sonar(beam_group_type=beam_group_type)
else:
tree_dict["Sonar"] = setgrouper.set_sonar()
tree_dict["Vendor_specific"] = setgrouper.set_vendor()
# Create tree and echodata
# TODO: make the creation of tree dynamically generated from yaml
tree = DataTree.from_dict(tree_dict, name="root")
echodata = EchoData(
source_file=file_chk, xml_path=xml_chk, sonar_model=sonar_model, parsed2zarr_obj=p2z
)
echodata._set_tree(tree)
echodata._load_tree()
return echodata
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,857 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/set_groups_azfp.py | """
Class to save unpacked echosounder data to appropriate groups in netcdf or zarr.
"""
from typing import List
import numpy as np
import xarray as xr
from ..utils.coding import set_time_encodings
from .set_groups_base import SetGroupsBase
class SetGroupsAZFP(SetGroupsBase):
"""Class for saving groups to netcdf or zarr from AZFP data files."""
# The sets beam_only_names, ping_time_only_names, and
# beam_ping_time_names are used in set_groups_base and
# in converting from v0.5.x to v0.6.0. The values within
# these sets are applied to all Sonar/Beam_groupX groups.
# 2023-07-24:
# PRs:
# - https://github.com/OSOceanAcoustics/echopype/pull/1056
# - https://github.com/OSOceanAcoustics/echopype/pull/1083
# Most of the artificially added beam and ping_time dimensions at v0.6.0
# were reverted at v0.8.0, due to concerns with efficiency and code clarity
# (see https://github.com/OSOceanAcoustics/echopype/issues/684 and
# https://github.com/OSOceanAcoustics/echopype/issues/978).
# However, the mechanisms to expand these dimensions were preserved for
# flexibility and potential later use.
# Note such expansion is still applied on AZFP data for 2 variables (see below).
# Variables that need only the beam dimension added to them.
beam_only_names = set()
# Variables that need only the ping_time dimension added to them.
# These variables do not change with ping_time in typical AZFP use cases,
# but we keep them here for consistency with EK60/EK80 EchoData formats
ping_time_only_names = {
"sample_interval",
"transmit_duration_nominal",
}
# Variables that need beam and ping_time dimensions added to them.
beam_ping_time_names = set()
beamgroups_possible = [
{
"name": "Beam_group1",
"descr": "contains backscatter power (uncalibrated) and other beam or channel-specific data.", # noqa
}
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# get frequency values
freq_old = list(self.parser_obj.unpacked_data["frequency"])
# sort the frequencies in ascending order
freq_new = freq_old[:]
freq_new.sort(reverse=False)
# obtain sorted frequency indices
self.freq_ind_sorted = [freq_new.index(ch) for ch in freq_old]
# obtain sorted frequencies
self.freq_sorted = self.parser_obj.unpacked_data["frequency"][self.freq_ind_sorted]
# obtain channel_ids
self.channel_ids_sorted = self._create_unique_channel_name()
# Put Frequency in Hz (this should be done after create_unique_channel_name)
self.freq_sorted = self.freq_sorted * 1000 # Frequency in Hz
def _create_unique_channel_name(self):
"""
Creates a unique channel name for AZFP sensor
using the variable unpacked_data created by
the AZFP parser
"""
serial_number = self.parser_obj.unpacked_data["serial_number"]
if serial_number.size == 1:
freq_as_str = self.freq_sorted.astype(int).astype(str)
# TODO: replace str(i+1) with Frequency Number from XML
channel_id = [
str(serial_number) + "-" + freq + "-" + str(i + 1)
for i, freq in enumerate(freq_as_str)
]
return channel_id
else:
raise NotImplementedError(
"Creating a channel name for more than"
+ " one serial number has not been implemented."
)
def set_env(self) -> xr.Dataset:
"""Set the Environment group."""
# TODO Look at why this cannot be encoded without the modifications
# @ngkavin: what modification?
ping_time = self.parser_obj.ping_time
ds = xr.Dataset(
{
"temperature": (
["time1"],
self.parser_obj.unpacked_data["temperature"],
{
"long_name": "Water temperature",
"standard_name": "sea_water_temperature",
"units": "deg_C",
},
)
},
coords={
"time1": (
["time1"],
ping_time,
{
"axis": "T",
"long_name": "Timestamp of each ping",
"standard_name": "time",
"comment": "Time coordinate corresponding to environmental variables.",
},
)
},
)
return set_time_encodings(ds)
def set_sonar(self) -> xr.Dataset:
"""Set the Sonar group."""
# Add beam_group and beam_group_descr variables sharing a common dimension
# (beam_group), using the information from self._beamgroups
self._beamgroups = self.beamgroups_possible
beam_groups_vars, beam_groups_coord = self._beam_groups_vars()
ds = xr.Dataset(beam_groups_vars, coords=beam_groups_coord)
# Assemble sonar group global attribute dictionary
sonar_attr_dict = {
"sonar_manufacturer": "ASL Environmental Sciences",
"sonar_model": self.sonar_model,
"sonar_serial_number": int(self.parser_obj.unpacked_data["serial_number"]),
"sonar_software_name": "AZFP",
# TODO: software version is hardwired. Read it from the XML file's AZFP_Version node
"sonar_software_version": "1.4",
"sonar_type": "echosounder",
}
ds = ds.assign_attrs(sonar_attr_dict)
return ds
def set_platform(self) -> xr.Dataset:
"""Set the Platform group."""
platform_dict = {"platform_name": "", "platform_type": "", "platform_code_ICES": ""}
unpacked_data = self.parser_obj.unpacked_data
time2 = self.parser_obj.ping_time
time1 = [time2[0]]
# If tilt_x and/or tilt_y are all nan, create single-value time2 dimension
# and single-value (np.nan) tilt_x and tilt_y
tilt_x = [np.nan] if np.isnan(unpacked_data["tilt_x"]).all() else unpacked_data["tilt_x"]
tilt_y = [np.nan] if np.isnan(unpacked_data["tilt_y"]).all() else unpacked_data["tilt_y"]
if (len(tilt_x) == 1 and np.isnan(tilt_x)) and (len(tilt_y) == 1 and np.isnan(tilt_y)):
time2 = [time2[0]]
ds = xr.Dataset(
{
"latitude": (
["time1"],
[np.nan],
self._varattrs["platform_var_default"]["latitude"],
),
"longitude": (
["time1"],
[np.nan],
self._varattrs["platform_var_default"]["longitude"],
),
"pitch": (
["time2"],
[np.nan] * len(time2),
self._varattrs["platform_var_default"]["pitch"],
),
"roll": (
["time2"],
[np.nan] * len(time2),
self._varattrs["platform_var_default"]["roll"],
),
"vertical_offset": (
["time2"],
[np.nan] * len(time2),
self._varattrs["platform_var_default"]["vertical_offset"],
),
"water_level": (
[],
np.nan,
self._varattrs["platform_var_default"]["water_level"],
),
"tilt_x": (
["time2"],
tilt_x,
{
"long_name": "Tilt X",
"units": "arc_degree",
},
),
"tilt_y": (
["time2"],
tilt_y,
{
"long_name": "Tilt Y",
"units": "arc_degree",
},
),
**{
var: (
["channel"],
[np.nan] * len(self.channel_ids_sorted),
self._varattrs["platform_var_default"][var],
)
for var in [
"transducer_offset_x",
"transducer_offset_y",
"transducer_offset_z",
]
},
**{
var: ([], np.nan, self._varattrs["platform_var_default"][var])
for var in [
"MRU_offset_x",
"MRU_offset_y",
"MRU_offset_z",
"MRU_rotation_x",
"MRU_rotation_y",
"MRU_rotation_z",
"position_offset_x",
"position_offset_y",
"position_offset_z",
]
},
"frequency_nominal": (
["channel"],
self.freq_sorted,
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
},
coords={
"channel": (
["channel"],
self.channel_ids_sorted,
self._varattrs["beam_coord_default"]["channel"],
),
"time1": (
["time1"],
# xarray and probably CF don't accept time coordinate variable with Nan values
time1,
{
**self._varattrs["platform_coord_default"]["time1"],
"comment": "Time coordinate corresponding to NMEA position data.",
},
),
"time2": (
["time2"],
time2,
{
"axis": "T",
"long_name": "Timestamps for platform motion and orientation data",
"standard_name": "time",
"comment": "Time coordinate corresponding to platform motion and "
"orientation data.",
},
),
},
)
ds = ds.assign_attrs(platform_dict)
return set_time_encodings(ds)
def set_beam(self) -> List[xr.Dataset]:
"""Set the Beam group."""
unpacked_data = self.parser_obj.unpacked_data
parameters = self.parser_obj.parameters
dig_rate = unpacked_data["dig_rate"][self.freq_ind_sorted] # dim: freq
ping_time = self.parser_obj.ping_time
# Build variables in the output xarray Dataset
N = [] # for storing backscatter_r values for each frequency
for ich in self.freq_ind_sorted:
N.append(
np.array(
[unpacked_data["counts"][p][ich] for p in range(len(unpacked_data["year"]))]
)
)
# Largest number of counts along the range dimension among the different channels
longest_range_sample = np.max(unpacked_data["num_bins"])
range_sample = np.arange(longest_range_sample)
# Pad power data
if any(unpacked_data["num_bins"] != longest_range_sample):
N_tmp = np.full((len(N), len(ping_time), longest_range_sample), np.nan)
for i, n in enumerate(N):
N_tmp[i, :, : n.shape[1]] = n
N = N_tmp
del N_tmp
tdn = (
unpacked_data["pulse_length"][self.freq_ind_sorted] / 1e6
) # Convert microseconds to seconds
range_samples_per_bin = unpacked_data["range_samples_per_bin"][
self.freq_ind_sorted
] # from data header
# Calculate sample interval in seconds
if len(dig_rate) == len(range_samples_per_bin):
# TODO: below only correct if range_samples_per_bin=1,
# need to implement p.86 for the case when averaging is used
sample_int = range_samples_per_bin / dig_rate
else:
# TODO: not sure what this error means
raise ValueError("dig_rate and range_samples not unique across frequencies")
ds = xr.Dataset(
{
"frequency_nominal": (
["channel"],
self.freq_sorted,
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
"beam_type": (
["channel"],
[0] * len(self.channel_ids_sorted),
{
"long_name": "Beam type",
"flag_values": [0, 1],
"flag_meanings": [
"Single beam",
"Split aperture beam",
],
},
),
**{
f"beam_direction_{var}": (
["channel"],
[np.nan] * len(self.channel_ids_sorted),
{
"long_name": f"{var}-component of the vector that gives the pointing "
"direction of the beam, in sonar beam coordinate "
"system",
"units": "1",
"valid_range": (-1.0, 1.0),
},
)
for var in ["x", "y", "z"]
},
"backscatter_r": (
["channel", "ping_time", "range_sample"],
np.array(N, dtype=np.float32),
{
"long_name": self._varattrs["beam_var_default"]["backscatter_r"][
"long_name"
],
"units": "count",
},
),
"equivalent_beam_angle": (
["channel"],
parameters["BP"][self.freq_ind_sorted],
{
"long_name": "Equivalent beam angle",
"units": "sr",
"valid_range": (0.0, 4 * np.pi),
},
),
"gain_correction": (
["channel"],
np.array(unpacked_data["gain"][self.freq_ind_sorted], dtype=np.float64),
{"long_name": "Gain correction", "units": "dB"},
),
"sample_interval": (
["channel"],
sample_int,
{
"long_name": "Interval between recorded raw data samples",
"units": "s",
"valid_min": 0.0,
},
),
"transmit_duration_nominal": (
["channel"],
tdn,
{
"long_name": "Nominal bandwidth of transmitted pulse",
"units": "s",
"valid_min": 0.0,
},
),
"transmit_frequency_start": (
["channel"],
self.freq_sorted,
self._varattrs["beam_var_default"]["transmit_frequency_start"],
),
"transmit_frequency_stop": (
["channel"],
self.freq_sorted,
self._varattrs["beam_var_default"]["transmit_frequency_stop"],
),
"transmit_type": (
[],
"CW",
{
"long_name": "Type of transmitted pulse",
"flag_values": ["CW"],
"flag_meanings": [
"Continuous Wave – a pulse nominally of one frequency",
],
},
),
"beam_stabilisation": (
[],
np.array(0, np.byte),
{
"long_name": "Beam stabilisation applied (or not)",
"flag_values": [0, 1],
"flag_meanings": ["not stabilised", "stabilised"],
},
),
"non_quantitative_processing": (
[],
np.array(0, np.int16),
{
"long_name": "Presence or not of non-quantitative processing applied"
" to the backscattering data (sonar specific)",
"flag_values": [0],
"flag_meanings": ["None"],
},
),
"sample_time_offset": (
[],
0.0,
{
"long_name": "Time offset that is subtracted from the timestamp"
" of each sample",
"units": "s",
},
),
},
coords={
"channel": (
["channel"],
self.channel_ids_sorted,
self._varattrs["beam_coord_default"]["channel"],
),
"ping_time": (
["ping_time"],
ping_time,
self._varattrs["beam_coord_default"]["ping_time"],
),
"range_sample": (
["range_sample"],
range_sample,
self._varattrs["beam_coord_default"]["range_sample"],
),
},
attrs={
"beam_mode": "",
"conversion_equation_t": "type_4",
},
)
# Manipulate some Dataset dimensions to adhere to convention
self.beam_groups_to_convention(
ds, self.beam_only_names, self.beam_ping_time_names, self.ping_time_only_names
)
return [set_time_encodings(ds)]
def set_vendor(self) -> xr.Dataset:
"""Set the Vendor_specific group."""
unpacked_data = self.parser_obj.unpacked_data
parameters = self.parser_obj.parameters
ping_time = self.parser_obj.ping_time
tdn = parameters["pulse_length"][self.freq_ind_sorted] / 1e6
anc = np.array(unpacked_data["ancillary"]) # convert to np array for easy slicing
# Build variables in the output xarray Dataset
Sv_offset = np.zeros_like(self.freq_sorted)
for ind, ich in enumerate(self.freq_ind_sorted):
# TODO: should not access the private function, better to compute Sv_offset in parser
Sv_offset[ind] = self.parser_obj._calc_Sv_offset(
self.freq_sorted[ind], unpacked_data["pulse_length"][ich]
)
ds = xr.Dataset(
{
"frequency_nominal": (
["channel"],
self.freq_sorted,
{
"units": "Hz",
"long_name": "Transducer frequency",
"valid_min": 0.0,
"standard_name": "sound_frequency",
},
),
# unpacked ping by ping data from 01A file
"digitization_rate": (
["channel"],
unpacked_data["dig_rate"][self.freq_ind_sorted],
{
"long_name": "Number of samples per second in kHz that is processed by the "
"A/D converter when digitizing the returned acoustic signal"
},
),
"lockout_index": (
["channel"],
unpacked_data["lockout_index"][self.freq_ind_sorted],
{
"long_name": "The distance, rounded to the nearest Bin Size after the "
"pulse is transmitted that over which AZFP will ignore echoes"
},
),
"number_of_bins_per_channel": (
["channel"],
unpacked_data["num_bins"][self.freq_ind_sorted],
{"long_name": "Number of bins per channel"},
),
"number_of_samples_per_average_bin": (
["channel"],
unpacked_data["range_samples_per_bin"][self.freq_ind_sorted],
{"long_name": "Range samples per bin for each channel"},
),
"board_number": (
["channel"],
unpacked_data["board_num"][self.freq_ind_sorted],
{"long_name": "The board the data came from channel 1-4"},
),
"data_type": (
["channel"],
unpacked_data["data_type"][self.freq_ind_sorted],
{
"long_name": "Datatype for each channel 1=Avg unpacked_data (5bytes), "
"0=raw (2bytes)"
},
),
"ping_status": (["ping_time"], unpacked_data["ping_status"]),
"number_of_acquired_pings": (
["ping_time"],
unpacked_data["num_acq_pings"],
{"long_name": "Pings acquired in the burst"},
),
"first_ping": (["ping_time"], unpacked_data["first_ping"]),
"last_ping": (["ping_time"], unpacked_data["last_ping"]),
"data_error": (
["ping_time"],
unpacked_data["data_error"],
{"long_name": "Error number if an error occurred"},
),
"sensors_flag": (["ping_time"], unpacked_data["sensor_flag"]),
"ancillary": (
["ping_time", "ancillary_len"],
unpacked_data["ancillary"],
{"long_name": "Tilt-X, Y, Battery, Pressure, Temperature"},
),
"ad_channels": (
["ping_time", "ad_len"],
unpacked_data["ad"],
{"long_name": "AD channel 6 and 7"},
),
"battery_main": (["ping_time"], unpacked_data["battery_main"]),
"battery_tx": (["ping_time"], unpacked_data["battery_tx"]),
"profile_number": (["ping_time"], unpacked_data["profile_number"]),
# unpacked ping by ping ancillary data from 01A file
"temperature_counts": (
["ping_time"],
anc[:, 4],
{"long_name": "Raw counts for temperature"},
),
"tilt_x_count": (["ping_time"], anc[:, 0], {"long_name": "Raw counts for Tilt-X"}),
"tilt_y_count": (["ping_time"], anc[:, 1], {"long_name": "Raw counts for Tilt-Y"}),
# unpacked data with dim len=0 from 01A file
"profile_flag": unpacked_data["profile_flag"],
"burst_interval": (
[],
unpacked_data["burst_int"],
{
"long_name": "Time in seconds between bursts or between pings if the burst"
" interval has been set equal to the ping period"
},
),
"ping_per_profile": (
[],
unpacked_data["ping_per_profile"],
{
"long_name": "Number of pings in a profile if ping averaging has been "
"selected"
}, # noqa
),
"average_pings_flag": (
[],
unpacked_data["avg_pings"],
{"long_name": "Flag indicating whether the pings average in time"},
),
"spare_channel": ([], unpacked_data["spare_chan"], {"long_name": "Spare channel"}),
"ping_period": (
[],
unpacked_data["ping_period"],
{"long_name": "Time between pings in a profile set"},
),
"phase": (
[],
unpacked_data["phase"],
{"long_name": "Phase number used to acquire the profile"},
),
"number_of_channels": (
[],
unpacked_data["num_chan"],
{"long_name": "Number of channels (1, 2, 3, or 4)"},
),
# parameters with channel dimension from XML file
"XML_transmit_duration_nominal": (
["channel"],
tdn,
{"long_name": "(From XML file) Nominal bandwidth of transmitted pulse"},
), # tdn comes from parameters
"XML_gain_correction": (
["channel"],
parameters["gain"][self.freq_ind_sorted],
{"long_name": "(From XML file) Gain correction"},
),
"XML_digitization_rate": (
["channel"],
parameters["dig_rate"][self.freq_ind_sorted],
{
"long_name": "(From XML file) Number of samples per second in kHz that is "
"processed by the A/D converter when digitizing the returned acoustic "
"signal"
},
),
"XML_lockout_index": (
["channel"],
parameters["lockout_index"][self.freq_ind_sorted],
{
"long_name": "(From XML file) The distance, rounded to the nearest "
"Bin Size after the pulse is transmitted that over which AZFP will "
"ignore echoes"
},
),
"DS": (["channel"], parameters["DS"][self.freq_ind_sorted]),
"EL": (
["channel"],
parameters["EL"][self.freq_ind_sorted],
{"long_name": "Sound pressure at the transducer", "units": "dB"},
),
"TVR": (
["channel"],
parameters["TVR"][self.freq_ind_sorted],
{
"long_name": "Transmit voltage response of the transducer",
"units": "dB re 1uPa/V at 1m",
},
),
"VTX": (
["channel"],
parameters["VTX"][self.freq_ind_sorted],
{"long_name": "Amplified voltage sent to the transducer"},
),
"Sv_offset": (["channel"], Sv_offset),
"number_of_samples_digitized_per_pings": (
["channel"],
parameters["range_samples"][self.freq_ind_sorted],
),
"number_of_digitized_samples_averaged_per_pings": (
["channel"],
parameters["range_averaging_samples"][self.freq_ind_sorted],
),
# parameters with dim len=0 from XML file
"XML_sensors_flag": parameters["sensors_flag"],
"XML_burst_interval": (
[],
parameters["burst_interval"],
{
"long_name": "Time in seconds between bursts or between pings if the burst "
"interval has been set equal to the ping period"
},
),
"XML_sonar_serial_number": parameters["serial_number"],
"number_of_frequency": parameters["num_freq"],
"number_of_pings_per_burst": parameters["pings_per_burst"],
"average_burst_pings_flag": parameters["average_burst_pings"],
# temperature coefficients from XML file
**{
f"temperature_k{var}": (
[],
parameters[f"k{var}"],
{"long_name": f"Thermistor bridge coefficient {var}"},
)
for var in ["a", "b", "c"]
},
**{
f"temperature_{var}": (
[],
parameters[var],
{"long_name": f"Thermistor calibration coefficient {var}"},
)
for var in ["A", "B", "C"]
},
# tilt coefficients from XML file
**{
f"tilt_X_{var}": (
[],
parameters[f"X_{var}"],
{"long_name": f"Calibration coefficient {var} for Tilt-X"},
)
for var in ["a", "b", "c", "d"]
},
**{
f"tilt_Y_{var}": (
[],
parameters[f"Y_{var}"],
{"long_name": f"Calibration coefficient {var} for Tilt-Y"},
)
for var in ["a", "b", "c", "d"]
},
},
coords={
"channel": (
["channel"],
self.channel_ids_sorted,
self._varattrs["beam_coord_default"]["channel"],
),
"ping_time": (
["ping_time"],
ping_time,
{
"axis": "T",
"long_name": "Timestamp of each ping",
"standard_name": "time",
},
),
"ancillary_len": (
["ancillary_len"],
list(range(len(unpacked_data["ancillary"][0]))),
),
"ad_len": (["ad_len"], list(range(len(unpacked_data["ad"][0])))),
},
)
return set_time_encodings(ds)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,858 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/commongrid/mvbs.py | """
Contains core functions needed to compute the MVBS of an input dataset.
"""
import warnings
from typing import Tuple, Union
import dask.array
import numpy as np
import xarray as xr
def get_bin_indices(
echo_range: np.ndarray, bins_er: np.ndarray, times: np.ndarray, bins_time: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""
Obtains the bin index of ``echo_range`` and ``times`` based
on the binning ``bins_er`` and ``bins_time``, respectively.
Parameters
----------
echo_range: np.ndarray
2D array of echo range values
bins_er: np.ndarray
1D array (used by np.digitize) representing the binning required for ``echo_range``
times: np.ndarray
1D array corresponding to the time values that should be binned
bins_time: np.ndarray
1D array (used by np.digitize) representing the binning required for ``times``
Returns
-------
digitized_echo_range: np.ndarray
2D array of bin indices for ``echo_range``
bin_time_ind: np.ndarray
1D array of bin indices for ``times``
"""
# get bin index for each echo range value
digitized_echo_range = np.digitize(echo_range, bins_er, right=False)
# turn datetime into integers, so we can use np.digitize
if isinstance(times, dask.array.Array):
times_i8 = times.compute().data.view("i8")
else:
times_i8 = times.view("i8")
# turn datetime into integers, so we can use np.digitize
bins_time_i8 = bins_time.view("i8")
# get bin index for each time
bin_time_ind = np.digitize(times_i8, bins_time_i8, right=False)
return digitized_echo_range, bin_time_ind
def bin_and_mean_echo_range(
arr: Union[np.ndarray, dask.array.Array], digitized_echo_range: np.ndarray, n_bin_er: int
) -> Union[np.ndarray, dask.array.Array]:
"""
Bins and means ``arr`` with respect to the ``echo_range`` bins.
Parameters
----------
arr: np.ndarray or dask.array.Array
2D array (dimension: [``echo_range`` x ``ping_time``]) to bin along ``echo_range``
and compute mean of each bin
digitized_echo_range: np.ndarray
2D array of bin indices for ``echo_range``
n_bin_er: int
The number of echo range bins
Returns
-------
er_means: np.ndarray or dask.array.Array
2D array representing the bin and mean of ``arr`` along ``echo_range``
"""
binned_means = []
for bin_er in range(1, n_bin_er):
# Catch a known warning that can occur, which does not impact the results
with warnings.catch_warnings():
# ignore warnings caused by taking a mean of an array filled with NaNs
warnings.filterwarnings(action="ignore", message="Mean of empty slice")
# bin and mean echo_range dimension
er_selected_data = np.nanmean(arr[:, digitized_echo_range == bin_er], axis=1)
# collect all echo_range bins
binned_means.append(er_selected_data)
# create full echo_range binned array
er_means = np.vstack(binned_means)
return er_means
def get_unequal_rows(mat: np.ndarray, row: np.ndarray) -> np.ndarray:
"""
Obtains those row indices of ``mat`` that are not equal
to ``row``.
Parameters
----------
mat: np.ndarray
2D array with the same column dimension as the number
of elements in ``row``
row: np.ndarray
1D array with the same number of element elements as
the column dimension of ``mat``
Returns
-------
row_ind_not_equal: np.ndarray
The row indices of ``mat`` that are not equal to ``row``
Notes
-----
Elements with NaNs are considered equal if they are in the same position.
"""
# compare row against all rows in mat (allowing for NaNs to be equal)
element_nan_equal = (mat == row) | (np.isnan(mat) & np.isnan(row))
# determine if mat row is equal to row
row_not_equal = np.logical_not(np.all(element_nan_equal, axis=1))
if isinstance(row_not_equal, dask.array.Array):
row_not_equal = row_not_equal.compute()
# get those row indices that are not equal to row
row_ind_not_equal = np.argwhere(row_not_equal).flatten()
return row_ind_not_equal
def if_all_er_steps_identical(er_chan: Union[xr.DataArray, np.ndarray]) -> bool:
"""
A comprehensive check that determines if all ``echo_range`` values
along ``ping_time`` have the same step size. If they do not have
the same step sizes, then grouping of the ``echo_range`` values
will be necessary.
Parameters
----------
er_chan: xr.DataArray or np.ndarray
2D array containing the ``echo_range`` values for each ``ping_time``
Returns
-------
bool
True, if grouping of ``echo_range`` along ``ping_time`` is necessary, otherwise False
Notes
-----
``er_chan`` should have rows corresponding to ``ping_time`` and columns
corresponding to ``range_sample``
"""
# grab the in-memory numpy echo_range values, if necessary
if isinstance(er_chan, xr.DataArray):
er_chan = er_chan.values
# grab the first ping_time that is not filled with NaNs
ping_index = 0
while np.all(np.isnan(er_chan[ping_index, :])):
ping_index += 1
# determine those rows of er_chan that are not equal to the row ping_index
unequal_ping_ind = get_unequal_rows(er_chan, er_chan[ping_index, :])
if len(unequal_ping_ind) > 0:
# see if all unequal_ping_ind are filled with NaNs
all_nans = np.all(np.all(np.isnan(er_chan[unequal_ping_ind, :]), axis=1))
if all_nans:
# All echo_range values have the same step size
return False
else:
# Some echo_range values have different step sizes
return True
else:
# All echo_range values have the same step size
return False
def if_last_er_steps_identical(er_chan: Union[xr.DataArray, np.ndarray]) -> bool:
"""
An alternative (less comprehensive) check that determines if all
``echo_range`` values along ``ping_time`` have the same step size.
If they do not have the same step sizes, then grouping of the
``echo_range`` values will be necessary.
Parameters
----------
er_chan: xr.DataArray or np.ndarray
2D array containing the ``echo_range`` values for each ``ping_time``
Returns
-------
bool
True, if grouping of ``echo_range`` along ``ping_time`` is necessary, otherwise False
Notes
-----
It is possible that this method will incorrectly determine if grouping
is necessary.
``er_chan`` should have rows corresponding to ``ping_time`` and columns
corresponding to ``range_sample``
"""
# determine the number of NaNs in each ping and find the unique number of NaNs
unique_num_nans = np.unique(np.isnan(er_chan.data).sum(axis=1))
# compute the results, if necessary, to allow for downstream checks
if isinstance(unique_num_nans, dask.array.Array):
unique_num_nans = unique_num_nans.compute()
# determine if any value is not 0 or er_chan.shape[1]
unexpected_num_nans = False in np.logical_or(
unique_num_nans == 0, unique_num_nans == er_chan.shape[1]
)
if unexpected_num_nans:
# echo_range varies with ping_time
return True
else:
# make sure that the final echo_range value for each ping_time is the same (account for NaN)
num_non_nans = np.logical_not(np.isnan(np.unique(er_chan.data[:, -1]))).sum()
# compute the results, if necessary, to allow for downstream checks
if isinstance(num_non_nans, dask.array.Array):
num_non_nans = num_non_nans.compute()
if num_non_nans > 1:
# echo_range varies with ping_time
return True
else:
# echo_range does not vary with ping_time
return False
def is_er_grouping_needed(
echo_range: Union[xr.DataArray, np.ndarray], comprehensive_er_check: bool
) -> bool:
"""
Determines if ``echo_range`` values along ``ping_time`` can change and
thus need to be grouped.
Parameters
----------
echo_range: xr.DataArray or np.ndarray
2D array containing the ``echo_range`` values for each ``ping_time``
comprehensive_er_check: bool
If True, a more comprehensive check will be completed to determine if ``echo_range``
grouping along ``ping_time`` is needed, otherwise a less comprehensive check will be done
Returns
-------
bool
If True grouping of ``echo_range`` will be required, else it will not
be necessary
"""
if comprehensive_er_check:
return if_all_er_steps_identical(echo_range)
else:
return if_last_er_steps_identical(echo_range)
def group_dig_er_bin_mean_echo_range(
arr: Union[np.ndarray, dask.array.Array],
digitized_echo_range: Union[np.ndarray, dask.array.Array],
n_bin_er: int,
) -> Union[np.ndarray, dask.array.Array]:
"""
Groups the rows of ``arr`` such that they have the same corresponding
row values in ``digitized_echo_range``, then applies ``bin_and_mean_echo_range``
on each group, and lastly assembles the correctly ordered ``er_means`` array
representing the bin and mean of ``arr`` with respect to ``echo_range``.
Parameters
----------
arr: dask.array.Array or np.ndarray
The 2D array whose values should be binned
digitized_echo_range: dask.array.Array or np.ndarray
2D array of bin indices for ``echo_range``
n_bin_er: int
The number of echo range bins
Returns
-------
er_means: dask.array.Array or np.ndarray
The bin and mean of ``arr`` with respect to ``echo_range``
"""
# compute bin indices to allow for downstream processes (mainly axis argument in unique)
if isinstance(digitized_echo_range, dask.array.Array):
digitized_echo_range = digitized_echo_range.compute()
# determine the unique rows of digitized_echo_range and the inverse
unique_er_bin_ind, unique_inverse = np.unique(digitized_echo_range, axis=0, return_inverse=True)
# create groups of row indices using the unique inverse
grps_same_ind = [
np.argwhere(unique_inverse == grp).flatten() for grp in np.unique(unique_inverse)
]
# for each group bin and mean arr along echo_range
# note: the values appended may not be in the correct final order
binned_er = []
for count, grp in enumerate(grps_same_ind):
binned_er.append(
bin_and_mean_echo_range(arr[grp, :], unique_er_bin_ind[count, :], n_bin_er)
)
# construct er_means and put the columns in the correct order
binned_er_array = np.hstack(binned_er)
correct_column_ind = np.argsort(np.concatenate(grps_same_ind))
er_means = binned_er_array[:, correct_column_ind]
return er_means
def bin_and_mean_2d(
arr: Union[dask.array.Array, np.ndarray],
bins_time: np.ndarray,
bins_er: np.ndarray,
times: np.ndarray,
echo_range: np.ndarray,
comprehensive_er_check: bool = True,
) -> np.ndarray:
"""
Bins and means ``arr`` based on ``times`` and ``echo_range``,
and their corresponding bins. If ``arr`` is ``Sv`` then this
will compute the MVBS.
Parameters
----------
arr: dask.array.Array or np.ndarray
The 2D array whose values should be binned
bins_time: np.ndarray
1D array (used by np.digitize) representing the binning required for ``times``
bins_er: np.ndarray
1D array (used by np.digitize) representing the binning required for ``echo_range``
times: np.ndarray
1D array corresponding to the time values that should be binned
echo_range: np.ndarray
2D array of echo range values
comprehensive_er_check: bool
If True, a more comprehensive check will be completed to determine if ``echo_range``
grouping along ``ping_time`` is needed, otherwise a less comprehensive check will be done
Returns
-------
final_reduced: np.ndarray
The final binned and mean ``arr``, if ``arr`` is ``Sv`` then this is the MVBS
Notes
-----
This function assumes that ``arr`` has rows corresponding to
``ping_time`` and columns corresponding to ``echo_range``.
This function should not be run if the number of ``echo_range`` values
vary amongst ``ping_times``. This should not occur for our current use
of echopype-generated Sv data.
"""
# get the number of echo range and time bins
n_bin_er = len(bins_er)
n_bin_time = len(bins_time)
# obtain the bin indices for echo_range and times
digitized_echo_range, bin_time_ind = get_bin_indices(echo_range, bins_er, times, bins_time)
# determine if grouping of echo_range values with the same step size is necessary
er_grouping_needed = is_er_grouping_needed(echo_range, comprehensive_er_check)
if er_grouping_needed:
# groups, bins, and means arr with respect to echo_range
er_means = group_dig_er_bin_mean_echo_range(arr, digitized_echo_range, n_bin_er)
else:
# bin and mean arr with respect to echo_range
er_means = bin_and_mean_echo_range(arr, digitized_echo_range[0, :], n_bin_er)
# if er_means is a dask array we compute it so the graph does not get too large
if isinstance(er_means, dask.array.Array):
er_means = er_means.compute()
# create final reduced array i.e. MVBS
final = np.empty((n_bin_time, n_bin_er - 1))
for bin_time in range(1, n_bin_time + 1):
# obtain er_mean indices corresponding to the time bin
indices = np.argwhere(bin_time_ind == bin_time).flatten()
if len(indices) == 0:
# fill values with NaN, if there are no values in the bin
final[bin_time - 1, :] = np.nan
else:
# bin and mean the er_mean time bin
final[bin_time - 1, :] = np.nanmean(er_means[:, indices], axis=1)
return final
def get_MVBS_along_channels(
ds_Sv: xr.Dataset, echo_range_interval: np.ndarray, ping_interval: np.ndarray
) -> np.ndarray:
"""
Computes the MVBS of ``ds_Sv`` along each channel for the given
intervals.
Parameters
----------
ds_Sv: xr.Dataset
A Dataset containing ``Sv`` and ``echo_range`` data with coordinates
``channel``, ``ping_time``, and ``range_sample``
echo_range_interval: np.ndarray
1D array (used by np.digitize) representing the binning required for ``echo_range``
ping_interval: np.ndarray
1D array (used by np.digitize) representing the binning required for ``ping_time``
Returns
-------
np.ndarray
The MVBS value of the input ``ds_Sv`` for all channels
Notes
-----
If the values in ``ds_Sv`` are delayed then the binning and mean of ``Sv`` with
respect to ``echo_range`` will take place, then the delayed result will be computed,
and lastly the binning and mean with respect to ``ping_time`` will be completed. It
is necessary to apply a compute midway through this method because Dask graph layers
get too large and this makes downstream operations very inefficient.
"""
all_MVBS = []
for chan in ds_Sv.channel:
# squeeze to remove "channel" dim if present
# TODO: not sure why not already removed for the AZFP case. Investigate.
ds = ds_Sv.sel(channel=chan).squeeze()
# average should be done in linear domain
sv = 10 ** (ds["Sv"] / 10)
# get MVBS for channel in linear domain
chan_MVBS = bin_and_mean_2d(
sv.data,
bins_time=ping_interval,
bins_er=echo_range_interval,
times=sv.ping_time.data,
echo_range=ds["echo_range"],
comprehensive_er_check=True,
)
# apply inverse mapping to get back to the original domain and store values
all_MVBS.append(10 * np.log10(chan_MVBS))
# collect the MVBS values for each channel
return np.stack(all_MVBS, axis=0)
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], 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["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", 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"/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,859 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/clean/test_noise.py | import numpy as np
import xarray as xr
import echopype as ep
import pytest
from numpy.random import default_rng
def test_remove_noise():
"""Test remove_noise on toy data"""
# Parameters for fake data
nchan, npings, nrange_samples = 1, 10, 100
chan = np.arange(nchan).astype(str)
ping_index = np.arange(npings)
range_sample = np.arange(nrange_samples)
data = np.ones(nrange_samples)
# Insert noise points
np.put(data, 30, -30)
np.put(data, 60, -30)
# Add more pings
data = np.array([data] * npings)
# Make DataArray
Sv = xr.DataArray(
[data],
coords=[
('channel', chan),
('ping_time', ping_index),
('range_sample', range_sample),
],
)
Sv.name = "Sv"
ds_Sv = Sv.to_dataset()
ds_Sv = ds_Sv.assign(
echo_range=xr.DataArray(
np.array([[np.linspace(0, 10, nrange_samples)] * npings]),
coords=Sv.coords,
)
)
ds_Sv = ds_Sv.assign(sound_absorption=0.001)
# Run noise removal
ds_Sv = ep.clean.remove_noise(
ds_Sv, ping_num=2, range_sample_num=5, SNR_threshold=0
)
# Test if noise points are nan
assert np.isnan(
ds_Sv.Sv_corrected.isel(channel=0, ping_time=0, range_sample=30)
)
assert np.isnan(
ds_Sv.Sv_corrected.isel(channel=0, ping_time=0, range_sample=60)
)
# Test remove noise on a normal distribution
np.random.seed(1)
data = np.random.normal(
loc=-100, scale=2, size=(nchan, npings, nrange_samples)
)
# Make Dataset to pass into remove_noise
Sv = xr.DataArray(
data,
coords=[
('channel', chan),
('ping_time', ping_index),
('range_sample', range_sample),
],
)
Sv.name = "Sv"
ds_Sv = Sv.to_dataset()
# Attach required echo_range and sound_absorption values
ds_Sv = ds_Sv.assign(
echo_range=xr.DataArray(
np.array([[np.linspace(0, 3, nrange_samples)] * npings]),
coords=Sv.coords,
)
)
ds_Sv = ds_Sv.assign(sound_absorption=0.001)
# Run noise removal
ds_Sv = ep.clean.remove_noise(
ds_Sv, ping_num=2, range_sample_num=5, SNR_threshold=0
)
null = ds_Sv.Sv_corrected.isnull()
# Test to see if the right number of points are removed before the range gets too large
assert (
np.count_nonzero(null.isel(channel=0, range_sample=slice(None, 50)))
== 6
)
def test_remove_noise_no_sound_absorption():
"""
Tests remove_noise on toy data that does
not have sound absorption as a variable.
"""
pytest.xfail(f"Tests for remove_noise have not been implemented" +
" when no sound absorption is provided!")
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"/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,860 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/convert/parse_base.py | import os
from collections import defaultdict
from datetime import datetime as dt
import numpy as np
from ..utils.log import _init_logger
from .utils.ek_raw_io import RawSimradFile, SimradEOF
FILENAME_DATETIME_EK60 = (
"(?P<survey>.+)?-?D(?P<date>\\w{1,8})-T(?P<time>\\w{1,6})-?(?P<postfix>\\w+)?.raw"
)
logger = _init_logger(__name__)
class ParseBase:
"""Parent class for all convert classes."""
def __init__(self, file, storage_options):
self.source_file = file
self.timestamp_pattern = None # regex pattern used to grab datetime embedded in filename
self.ping_time = [] # list to store ping time
self.storage_options = storage_options
self.zarr_datagrams = [] # holds all parsed datagrams
def _print_status(self):
"""Prints message to console giving information about the raw file being parsed."""
class ParseEK(ParseBase):
"""Class for converting data from Simrad echosounders."""
def __init__(self, file, params, storage_options, dgram_zarr_vars):
super().__init__(file, storage_options)
# Parent class attributes
# regex pattern used to grab datetime embedded in filename
self.timestamp_pattern = FILENAME_DATETIME_EK60
# Class attributes
self.config_datagram = None
self.ping_data_dict = defaultdict(lambda: defaultdict(list)) # ping data
self.ping_data_dict_tx = defaultdict(lambda: defaultdict(list)) # transmit ping data
self.ping_time = defaultdict(list) # store ping time according to channel
self.num_range_sample_groups = None # number of range_sample groups
self.ch_ids = defaultdict(
list
) # Stores the channel ids for each data type (power, angle, complex)
self.data_type = self._select_datagrams(params)
self.nmea = defaultdict(list) # Dictionary to store NMEA data(timestamp and string)
self.mru = defaultdict(list) # Dictionary to store MRU data (heading, pitch, roll, heave)
self.fil_coeffs = defaultdict(dict) # Dictionary to store PC and WBT coefficients
self.fil_df = defaultdict(dict) # Dictionary to store filter decimation factors
self.CON1_datagram = None # Holds the ME70 CON1 datagram
# dgram vars and their associated dims that should be written directly to zarr
self.dgram_zarr_vars = dgram_zarr_vars
def _print_status(self):
time = dt.utcfromtimestamp(self.config_datagram["timestamp"].tolist() / 1e9).strftime(
"%Y-%b-%d %H:%M:%S"
)
logger.info(
f"parsing file {os.path.basename(self.source_file)}, " f"time of first ping: {time}"
)
def rectangularize_data(self):
"""
Rectangularize the power, angle, and complex data.
Additionally, convert the data to a numpy array
indexed by channel.
"""
# append zarr datagrams to channel ping data
for dgram in self.zarr_datagrams:
self._append_channel_ping_data(dgram, zarr_vars=False)
# Rectangularize all data and convert to numpy array indexed by channel
for data_type in ["power", "angle", "complex"]:
# Receive data
for k, v in self.ping_data_dict[data_type].items():
if all(
(x is None) or (x.size == 0) for x in v
): # if no data in a particular channel
self.ping_data_dict[data_type][k] = None
else:
# Sort complex and power/angle channels and pad NaN
self.ch_ids[data_type].append(k)
self.ping_data_dict[data_type][k] = self.pad_shorter_ping(v)
# Transmit data
self.rectangularize_transmit_ping_data(data_type)
def rectangularize_transmit_ping_data(self, data_type: str) -> None:
"""
Rectangularize the ``data_type`` data within transmit ping data.
Additionally, convert the data to a numpy array
indexed by channel.
Parameters
----------
data_type: str
The key of ``self.ping_data_dict_tx`` to rectangularize
"""
# Transmit data
for k, v in self.ping_data_dict_tx[data_type].items():
if all((x is None) or (x.size == 0) for x in v): # if no data in a particular channel
self.ping_data_dict_tx[data_type][k] = None
else:
self.ping_data_dict_tx[data_type][k] = self.pad_shorter_ping(v)
def parse_raw(self):
"""Parse raw data file from Simrad EK60, EK80, and EA640 echosounders."""
with RawSimradFile(self.source_file, "r", storage_options=self.storage_options) as fid:
self.config_datagram = fid.read(1)
self.config_datagram["timestamp"] = np.datetime64(
self.config_datagram["timestamp"].replace(tzinfo=None), "[ns]"
)
if "configuration" in self.config_datagram:
for v in self.config_datagram["configuration"].values():
if "pulse_duration" not in v and "pulse_length" in v:
# it seems like sometimes this field can appear with the name "pulse_length"
# and in the form of floats separated by semicolons
v["pulse_duration"] = [float(x) for x in v["pulse_length"].split(";")]
# If exporting to XML file (EK80/EA640 only), print a message
if "print_export_msg" in self.data_type:
if "ENV" in self.data_type:
xml_type = "environment"
elif "CONFIG" in self.data_type:
xml_type = "configuration"
logger.info(f"exporting {xml_type} XML file")
# Don't parse anything else if only the config xml is required.
if "CONFIG" in self.data_type:
return
# If not exporting to XML, print the usual converting message
else:
self._print_status()
# Check if reading an ME70 file with a CON1 datagram.
next_datagram = fid.peek()
if next_datagram == "CON1":
self.CON1_datagram = fid.read(1)
else:
self.CON1_datagram = None
# IDs of the channels found in the dataset
# self.ch_ids = list(self.config_datagram['configuration'].keys())
# Read the rest of datagrams
self._read_datagrams(fid)
if "ALL" in self.data_type:
# Convert ping time to 1D numpy array, stored in dict indexed by channel,
# this will help merge data from all channels into a cube
for ch, val in self.ping_time.items():
self.ping_time[ch] = np.array(val, dtype="datetime64[ns]")
def _read_datagrams(self, fid):
"""Read all datagrams.
A sample EK60 RAW0 datagram:
{'type': 'RAW0',
'low_date': 71406392,
'high_date': 30647127,
'channel': 1,
'mode': 3,
'transducer_depth': 9.149999618530273,
'frequency': 18000.0,
'transmit_power': 2000.0,
'pulse_length': 0.0010239999974146485,
'bandwidth': 1573.66552734375,
'sample_interval': 0.00025599999935366213,
'sound_velocity': 1466.0,
'absorption_coefficient': 0.0030043544247746468,
'heave': 0.0,
'roll': 0.0,
'pitch': 0.0,
'temperature': 4.0,
'heading': 0.0,
'transmit_mode': 1,
'spare0': '\x00\x00\x00\x00\x00\x00',
'offset': 0,
'count': 1386,
'timestamp': numpy.datetime64('2018-02-11T16:40:25.276'),
'bytes_read': 5648,
'power': array([ -6876, -8726, -11086, ..., -11913, -12522, -11799], dtype=int16),
'angle': array([[ 110, 13],
[ 3, -4],
[ -54, -65],
...,
[ -92, -107],
[-104, -122],
[ 82, 74]], dtype=int8)}
A sample EK80 XML-parameter datagram:
{'channel_id': 'WBT 545612-15 ES200-7C',
'channel_mode': 0,
'pulse_form': 1,
'frequency_start': '160000',
'frequency_end': '260000',
'pulse_duration': 0.001024,
'sample_interval': 5.33333333333333e-06,
'transmit_power': 15.0,
'slope': 0.01220703125}
A sample EK80 XML-environment datagram:
{'type': 'XML0',
'low_date': 3137819385,
'high_date': 30616609,
'timestamp': numpy.datetime64('2017-09-12T23:49:10.723'),
'bytes_read': 448,
'subtype': 'environment',
'environment': {'depth': 240.0,
'acidity': 8.0,
'salinity': 33.7,
'sound_speed': 1486.4,
'temperature': 6.9,
'latitude': 45.0,
'sound_velocity_profile': [1.0, 1486.4, 1000.0, 1486.4],
'sound_velocity_source': 'Manual',
'drop_keel_offset': 0.0,
'drop_keel_offset_is_manual': 0,
'water_level_draft': 0.0,
'water_level_draft_is_manual': 0,
'transducer_name': 'Unknown',
'transducer_sound_speed': 1490.0},
'xml': '<?xml version="1.0" encoding="utf-8"?>\r\n<Environment Depth="240" ... />\r\n</Environment>'}
""" # noqa
num_datagrams_parsed = 0
while True:
try:
# TODO: @ngkvain: what I need in the code to not PARSE the raw0/3 datagram
# when users only want CONFIG or ENV, but the way this is implemented
# the raw0/3 datagrams are still parsed, you are just not saving them
new_datagram = fid.read(1)
except SimradEOF:
break
# Convert the timestamp to a datetime64 object.
new_datagram["timestamp"] = np.datetime64(
new_datagram["timestamp"].replace(tzinfo=None), "[ns]"
)
num_datagrams_parsed += 1
# Skip any datagram that the user does not want to save
if (
not any(new_datagram["type"].startswith(dgram) for dgram in self.data_type)
and "ALL" not in self.data_type
):
continue
# XML datagrams store environment or instrument parameters for EK80
if new_datagram["type"].startswith("XML"):
if new_datagram["subtype"] == "environment" and (
"ENV" in self.data_type or "ALL" in self.data_type
):
self.environment = new_datagram["environment"]
self.environment["xml"] = new_datagram["xml"]
self.environment["timestamp"] = new_datagram["timestamp"]
# Don't parse anything else if only the environment xml is required.
if "ENV" in self.data_type:
break
elif new_datagram["subtype"] == "parameter" and ("ALL" in self.data_type):
current_parameters = new_datagram["parameter"]
# RAW0 datagrams store raw acoustic data for a channel for EK60
elif new_datagram["type"].startswith("RAW0"):
# Save channel-specific ping time. The channels are stored as 1-based indices
self.ping_time[new_datagram["channel"]].append(new_datagram["timestamp"])
# Append ping by ping data
self._append_channel_ping_data(new_datagram)
# EK80 datagram sequence:
# - XML0 pingsequence
# - XML0 parameter
# - RAW4
# - RAW3
# RAW3 datagrams store raw acoustic data for a channel for EK80
elif new_datagram["type"].startswith("RAW3"):
curr_ch_id = new_datagram["channel_id"]
# Check if the proceeding Parameter XML does not
# match with data in this RAW3 datagram
if current_parameters["channel_id"] != curr_ch_id:
raise ValueError("Parameter ID does not match RAW")
# Save channel-specific ping time
self.ping_time[curr_ch_id].append(new_datagram["timestamp"])
# Append ping by ping data
new_datagram.update(current_parameters)
self._append_channel_ping_data(new_datagram)
# RAW4 datagrams store raw transmit pulse for a channel for EK80
elif new_datagram["type"].startswith("RAW4"):
curr_ch_id = new_datagram["channel_id"]
# Check if the proceeding Parameter XML does not
# match with data in this RAW4 datagram
if current_parameters["channel_id"] != curr_ch_id:
raise ValueError("Parameter ID does not match RAW")
# Ping time is identical to the immediately following RAW3 datagram
# so does not need to be stored separately
# Append ping by ping data
new_datagram.update(current_parameters)
self._append_channel_ping_data(new_datagram, rx=False)
# NME datagrams store ancillary data as NMEA-0817 style ASCII data.
elif new_datagram["type"].startswith("NME"):
self.nmea["timestamp"].append(new_datagram["timestamp"])
self.nmea["nmea_string"].append(new_datagram["nmea_string"])
# MRU datagrams contain motion data for each ping for EK80
elif new_datagram["type"].startswith("MRU"):
self.mru["heading"].append(new_datagram["heading"])
self.mru["pitch"].append(new_datagram["pitch"])
self.mru["roll"].append(new_datagram["roll"])
self.mru["heave"].append(new_datagram["heave"])
self.mru["timestamp"].append(new_datagram["timestamp"])
# FIL datagrams contain filters for processing bascatter data for EK80
elif new_datagram["type"].startswith("FIL"):
self.fil_coeffs[new_datagram["channel_id"]][new_datagram["stage"]] = new_datagram[
"coefficients"
]
self.fil_df[new_datagram["channel_id"]][new_datagram["stage"]] = new_datagram[
"decimation_factor"
]
# TAG datagrams contain time-stamped annotations inserted via the recording software
elif new_datagram["type"].startswith("TAG"):
logger.info("TAG datagram encountered.")
# BOT datagrams contain sounder detected bottom depths from .bot files
elif new_datagram["type"].startswith("BOT"):
logger.info("BOT datagram encountered.")
# DEP datagrams contain sounder detected bottom depths from .out files
# as well as reflectivity data
elif new_datagram["type"].startswith("DEP"):
logger.info("DEP datagram encountered.")
else:
logger.info("Unknown datagram type: " + str(new_datagram["type"]))
def _append_zarr_dgram(self, full_dgram: dict):
"""
Selects a subset of the datagram values that
need to be sent directly to a zarr file and
appends them to the class variable ``zarr_datagrams``.
Additionally, if any power data exists, the
conversion factor will be applied to it.
Parameters
----------
full_dgram : dict
Successfully parsed datagram containing at least
one variable that should be written to a zarr file
Returns
-------
reduced_datagram : dict
A reduced datagram containing only those variables
that should be written to a zarr file and their
associated dimensions.
"""
wanted_vars = set()
for key in self.dgram_zarr_vars.keys():
wanted_vars = wanted_vars.union({key, *self.dgram_zarr_vars[key]})
# construct reduced datagram
reduced_datagram = {key: full_dgram[key] for key in wanted_vars if key in full_dgram.keys()}
# apply conversion factor to power data, if it exists
if ("power" in reduced_datagram.keys()) and (
isinstance(reduced_datagram["power"], np.ndarray)
):
# Manufacturer-specific power conversion factor
INDEX2POWER = 10.0 * np.log10(2.0) / 256.0
reduced_datagram["power"] = reduced_datagram["power"].astype("float32") * INDEX2POWER
if reduced_datagram:
self.zarr_datagrams.append(reduced_datagram)
def _append_channel_ping_data(self, datagram, rx=True, zarr_vars=True):
"""
Append ping by ping data.
Parameters
----------
datagram : dict
the newly read sample datagram
rx : bool
whether this is receive ping data
zarr_vars : bool
whether one should account for zarr vars
"""
# TODO: do a thorough check with the convention and processing
# unsaved = ['channel', 'channel_id', 'low_date', 'high_date', # 'offset', 'frequency' ,
# 'transmit_mode', 'spare0', 'bytes_read', 'type'] #, 'n_complex']
ch_id = datagram["channel_id"] if "channel_id" in datagram else datagram["channel"]
# append zarr variables, if they exist
if zarr_vars and rx:
common_vars = set(self.dgram_zarr_vars.keys()).intersection(set(datagram.keys()))
if common_vars:
self._append_zarr_dgram(datagram)
for var in common_vars:
del datagram[var]
for k, v in datagram.items():
if rx:
self.ping_data_dict[k][ch_id].append(v)
else:
self.ping_data_dict_tx[k][ch_id].append(v)
@staticmethod
def pad_shorter_ping(data_list) -> np.ndarray:
"""
Pad shorter ping with NaN: power, angle, complex samples.
Parameters
----------
data_list : list
Power, angle, or complex samples for each channel from RAW3 datagram.
Each ping is one entry in the list.
Returns
-------
out_array : np.ndarray
Numpy array containing samplings from all pings.
The array is NaN-padded if some pings are of different lengths.
"""
lens = np.array([len(item) for item in data_list])
if np.unique(lens).size != 1: # if some pings have different lengths along range
if data_list[0].ndim == 2:
# Angle data have an extra dimension for alongship and athwartship samples
mask = lens[:, None, None] > np.array([np.arange(lens.max())] * 2).T
else:
mask = lens[:, None] > np.arange(lens.max())
# Take care of problem of np.nan being implicitly "real"
if data_list[0].dtype in {np.dtype("complex64"), np.dtype("complex128")}:
out_array = np.full(mask.shape, np.nan + 0j)
else:
out_array = np.full(mask.shape, np.nan)
# Fill in values
out_array[mask] = np.concatenate(data_list).reshape(-1) # reshape in case data > 1D
else:
out_array = np.array(data_list)
return out_array
def _select_datagrams(self, params):
"""Translates user input into specific datagrams or ALL
Valid use cases:
# get GPS info only (EK60, EK80)
# ec.to_netcdf(data_type='GPS')
# get configuration XML only (EK80)
# ec.to_netcdf(data_type='CONFIG')
# get environment XML only (EK80)
# ec.to_netcdf(data_type='ENV')
"""
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,861 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/tests/convert/test_parsed_to_zarr.py | import pytest
import xarray as xr
from typing import List, Tuple
from echopype import open_raw
from pathlib import Path
import os.path
@pytest.fixture
def ek60_path(test_path):
return test_path['EK60']
def compare_zarr_vars(ed_zarr: xr.Dataset, ed_no_zarr: xr.Dataset,
var_to_comp: List[str], ed_path) -> Tuple[xr.Dataset, xr.Dataset]:
"""
Compares the dask variables in ``ed_zarr`` against their
counterparts in ``ed_no_zarr`` by computing the dask results
and using xarray to make sure the variables are identical.
Additionally, this function will drop all of these compared
variables.
Parameters
----------
ed_zarr : xr.Dataset
EchoData object with variables that were written directly
to a zarr and then loaded with dask
ed_no_zarr : xr.Dataset
An in-memory EchoData object
var_to_comp : List[str]
List representing those variables that were written directly
to a zarr and then loaded with dask
ed_path : str
EchoData group (e.g. "Sonar/Beam_group1")
Returns
-------
Tuple[xr.Dataset, xr.Dataset]
Datasets ``ed_zarr`` and ``ed_no_zarr``, respectively with
``var_to_comp`` removed.
"""
for var in var_to_comp:
for chan in ed_zarr[ed_path][var].channel:
# here we compute to make sure values are being compared, rather than just shapes
var_zarr = ed_zarr[ed_path][var].sel(channel=chan).compute()
var_no_zarr = ed_no_zarr[ed_path][var].sel(channel=chan)
assert var_zarr.identical(var_no_zarr)
ed_zarr[ed_path] = ed_zarr[ed_path].drop_vars(var_to_comp)
ed_no_zarr[ed_path] = ed_no_zarr[ed_path].drop_vars(var_to_comp)
return ed_zarr, ed_no_zarr
@pytest.mark.parametrize(
["raw_file", "sonar_model", "use_swap"],
[
("L0003-D20040909-T161906-EK60.raw", "EK60", True),
pytest.param(
"L0003-D20040909-T161906-EK60.raw",
"EK60",
False,
marks=pytest.mark.xfail(
run=False,
reason="Expected out of memory error. See https://github.com/OSOceanAcoustics/echopype/issues/489",
),
),
],
ids=["noaa_offloaded", "noaa_not_offloaded"],
)
def test_raw2zarr(raw_file, sonar_model, use_swap, ek60_path):
"""Tests for memory expansion relief"""
import os
from tempfile import TemporaryDirectory
from echopype.echodata.echodata import EchoData
name = os.path.basename(raw_file).replace('.raw', '')
fname = f"{name}__{use_swap}.zarr"
file_path = ek60_path / raw_file
echodata = open_raw(
raw_file=file_path,
sonar_model=sonar_model,
use_swap=use_swap
)
# Most likely succeed if it doesn't crash
assert isinstance(echodata, EchoData)
with TemporaryDirectory() as tmpdir:
output_save_path = os.path.join(tmpdir, fname)
echodata.to_zarr(output_save_path)
# If it goes all the way to here it is most likely successful
assert os.path.exists(output_save_path)
if use_swap:
# create a copy of zarr_file_name. The join is necessary so that we are not referencing zarr_file_name
temp_zarr_path = ''.join(echodata.parsed2zarr_obj.zarr_file_name)
del echodata
# make sure that the temporary zarr was deleted
assert Path(temp_zarr_path).exists() is False
@pytest.mark.parametrize(
["path_model", "raw_file", "sonar_model"],
[
("EK60", os.path.join("ncei-wcsd", "Summer2017-D20170615-T190214.raw"), "EK60"),
("EK60", "DY1002_EK60-D20100318-T023008_rep_freq.raw", "EK60"),
("EK80", "Summer2018--D20180905-T033113.raw", "EK80"),
("EK80_CAL", "2018115-D20181213-T094600.raw", "EK80"),
("EK80", "Green2.Survey2.FM.short.slow.-D20191004-T211557.raw", "EK80"),
("EK80", "2019118 group2survey-D20191214-T081342.raw", "EK80"),
],
ids=["ek60_summer_2017", "ek60_rep_freq", "ek80_summer_2018",
"ek80_bb_w_cal", "ek80_short_slow", "ek80_grp_2_survey"],
)
def test_direct_to_zarr_integration(path_model: str, raw_file: str,
sonar_model: str, test_path: dict) -> None:
"""
Integration Test that ensure writing variables
directly to a temporary zarr store and then assigning
them to the EchoData object create an EchoData object
that is identical to the method of not writing directly
to a zarr.
Parameters
----------
path_model: str
The key in ``test_path`` pointing to the appropriate
directory containing ``raw_file``
raw_file: str
The raw file to test
sonar_model: str
The sonar model corresponding to ``raw_file``
test_path: dict
A dictionary of all the model paths.
Notes
-----
This test should only be conducted with small raw files
as DataSets must be loaded into RAM!
"""
raw_file_path = test_path[path_model] / raw_file
ed_zarr = open_raw(raw_file_path, sonar_model=sonar_model, use_swap=True, max_mb=100)
ed_no_zarr = open_raw(raw_file_path, sonar_model=sonar_model, use_swap=False)
for grp in ed_zarr.group_paths:
# remove conversion time so we can do a direct comparison
if "conversion_time" in ed_zarr[grp].attrs:
del ed_zarr[grp].attrs["conversion_time"]
del ed_no_zarr[grp].attrs["conversion_time"]
# Compare angle, power, complex, if zarr drop the zarr variables and compare datasets
if grp == "Sonar/Beam_group2":
var_to_comp = ['angle_athwartship', 'angle_alongship', 'backscatter_r']
ed_zarr, ed_no_zarr = compare_zarr_vars(ed_zarr, ed_no_zarr, var_to_comp, grp)
if grp == "Sonar/Beam_group1":
if 'backscatter_i' in ed_zarr[grp]:
var_to_comp = ['backscatter_r', 'backscatter_i']
else:
var_to_comp = ['angle_athwartship', 'angle_alongship', 'backscatter_r']
ed_zarr, ed_no_zarr = compare_zarr_vars(ed_zarr, ed_no_zarr, var_to_comp, grp)
assert ed_zarr[grp].identical(ed_no_zarr[grp])
# create a copy of zarr_file_name. The join is necessary so that we are not referencing zarr_file_name
temp_zarr_path = ''.join(ed_zarr.parsed2zarr_obj.zarr_file_name)
del ed_zarr
# make sure that the temporary zarr was deleted
assert Path(temp_zarr_path).exists() is False
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/set_groups_ek80.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/consolidate/api.py": ["/echopype/calibrate/ek80_complex.py", "/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/io.py", "/echopype/utils/prov.py", "/echopype/consolidate/split_beam_angle.py"], "/echopype/convert/__init__.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_base.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/utils/prov.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_ek80.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/echodata/test_echodata_simrad.py": ["/echopype/echodata/simrad.py"], "/echopype/tests/utils/test_coding.py": ["/echopype/utils/coding.py"], "/echopype/echodata/convention/conv.py": ["/echopype/echodata/__init__.py"], "/echopype/metrics/__init__.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/tests/calibrate/test_calibrate_ek80.py": ["/echopype/__init__.py"], "/echopype/tests/commongrid/test_nasc.py": ["/echopype/__init__.py", "/echopype/calibrate/__init__.py", "/echopype/commongrid/__init__.py", "/echopype/commongrid/nasc.py", "/echopype/consolidate/__init__.py"], "/echopype/convert/api.py": ["/echopype/core.py", "/echopype/convert/parsed_to_zarr.py", "/echopype/echodata/echodata.py", "/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/utils/prov.py"], "/echopype/convert/set_groups_azfp.py": ["/echopype/utils/coding.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/clean/test_noise.py": ["/echopype/__init__.py"], "/echopype/convert/parse_base.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_raw_io.py"], "/echopype/tests/convert/test_parsed_to_zarr.py": ["/echopype/__init__.py", "/echopype/echodata/echodata.py"], "/echopype/echodata/widgets/widgets.py": ["/echopype/echodata/widgets/utils.py"], "/echopype/tests/echodata/test_echodata_structure.py": ["/echopype/echodata/echodata.py", "/echopype/echodata/api.py"], "/echopype/tests/calibrate/test_range_integration.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_env_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/convert/test_convert_source_target_locs.py": ["/echopype/__init__.py", "/echopype/utils/coding.py"], "/echopype/tests/utils/test_utils_uwa.py": ["/echopype/utils/uwa.py"], "/echopype/utils/io.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/core.py"], "/echopype/tests/calibrate/test_ek80_complex.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/convert/set_groups_ek60.py": ["/echopype/utils/coding.py", "/echopype/utils/log.py", "/echopype/convert/set_groups_base.py"], "/echopype/convert/utils/ek_raw_io.py": ["/echopype/utils/log.py"], "/echopype/convert/parse_azfp.py": ["/echopype/utils/log.py", "/echopype/convert/parse_base.py"]} |
73,862 | OSOceanAcoustics/echopype | refs/heads/main | /echopype/echodata/widgets/widgets.py | import datetime
import html
from functools import lru_cache
from pathlib import Path
import pkg_resources
from jinja2 import Environment, FileSystemLoader, Template
from jinja2.exceptions import TemplateNotFound
from .utils import _single_node_repr, hash_value, html_repr, make_key
FILTERS = {
"datetime_from_timestamp": datetime.datetime.fromtimestamp,
"html_escape": html.escape,
"type": type,
"repr": repr,
"html_repr": html_repr,
"hash_value": hash_value,
"make_key": make_key,
"node_repr": _single_node_repr,
}
HERE = Path(__file__).parent
STATIC_DIR = HERE / "static"
TEMPLATE_PATHS = [HERE / "templates"]
STATIC_FILES = (
"static/html/icons-svg-inline.html",
"static/css/style.css",
)
@lru_cache(None)
def _load_static_files():
"""Lazily load the resource files into memory the first time they are needed.
Clone from xarray.core.formatted_html_template.
"""
return [pkg_resources.resource_string(__name__, fname).decode("utf8") for fname in STATIC_FILES]
def get_environment() -> Environment:
loader = FileSystemLoader(TEMPLATE_PATHS)
environment = Environment(loader=loader)
environment.filters.update(FILTERS)
return environment
def get_template(name: str) -> Template:
try:
return get_environment().get_template(name)
except TemplateNotFound as e:
raise TemplateNotFound(
f"Unable to find {name} in echopype.echodata.widgets. TEMPLATE_PATHS {TEMPLATE_PATHS}"
) from e
| {"/echopype/convert/set_groups_ad2cp.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/convert/parse_ad2cp.py", "/echopype/convert/set_groups_base.py"], "/echopype/tests/utils/test_source_filenames.py": ["/echopype/utils/prov.py"], "/echopype/echodata/convention/__init__.py": ["/echopype/echodata/convention/conv.py"], "/echopype/consolidate/split_beam_angle.py": ["/echopype/calibrate/ek80_complex.py"], "/echopype/calibrate/ecs.py": ["/echopype/utils/log.py"], "/echopype/tests/calibrate/test_ecs_integration.py": ["/echopype/__init__.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/cal_params.py"], "/echopype/calibrate/api.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/convert/parse_ad2cp.py": ["/echopype/convert/parse_base.py"], "/echopype/echodata/combine.py": ["/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/echodata.py"], "/echopype/tests/consolidate/test_consolidate_integration.py": ["/echopype/__init__.py"], "/echopype/echodata/api.py": ["/echopype/echodata/echodata.py", "/echopype/core.py"], "/echopype/tests/utils/test_utils_io.py": ["/echopype/utils/io.py"], "/echopype/echodata/sensor_ep_version_mapping/ep_version_mapper.py": ["/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py"], "/echopype/tests/mask/test_mask.py": ["/echopype/__init__.py", "/echopype/mask/__init__.py", "/echopype/mask/api.py"], "/echopype/tests/conftest.py": ["/echopype/testing.py"], "/echopype/tests/echodata/utils.py": ["/echopype/convert/set_groups_base.py", "/echopype/echodata/echodata.py"], "/echopype/tests/commongrid/test_mvbs.py": ["/echopype/__init__.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/calibrate/test_cal_params_integration.py": ["/echopype/__init__.py"], "/echopype/tests/test_core.py": ["/echopype/core.py"], "/echopype/calibrate/calibrate_azfp.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/echodata/widgets/utils.py": ["/echopype/echodata/convention/utils.py"], "/echopype/echodata/simrad.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek60.py": ["/echopype/__init__.py"], "/echopype/convert/parsed_to_zarr_ek60.py": ["/echopype/convert/parsed_to_zarr.py"], "/echopype/mask/api.py": ["/echopype/utils/io.py", "/echopype/utils/prov.py"], "/echopype/qc/__init__.py": ["/echopype/qc/api.py"], "/echopype/tests/utils/test_processinglevels_integration.py": ["/echopype/__init__.py"], "/echopype/tests/metrics/test_metrics_summary_statistics.py": ["/echopype/metrics/summary_statistics.py"], "/echopype/__init__.py": ["/echopype/convert/api.py", "/echopype/echodata/api.py", "/echopype/echodata/combine.py", "/echopype/utils/io.py", "/echopype/utils/log.py"], "/.ci_helpers/check-version.py": ["/echopype/__init__.py"], "/echopype/calibrate/ek80_complex.py": ["/echopype/convert/set_groups_ek80.py"], "/echopype/consolidate/__init__.py": ["/echopype/consolidate/api.py"], "/echopype/commongrid/api.py": ["/echopype/utils/prov.py", "/echopype/commongrid/mvbs.py"], "/echopype/tests/echodata/test_echodata_combine.py": ["/echopype/__init__.py", "/echopype/utils/coding.py", "/echopype/echodata/__init__.py", "/echopype/echodata/combine.py"], "/echopype/tests/utils/test_utils_log.py": ["/echopype/__init__.py"], "/echopype/mask/__init__.py": ["/echopype/mask/api.py"], "/echopype/tests/convert/test_convert_ad2cp.py": ["/echopype/__init__.py", "/echopype/testing.py"], "/echopype/convert/parsed_to_zarr_ek80.py": ["/echopype/convert/parsed_to_zarr_ek60.py"], "/echopype/tests/visualize/test_plot.py": ["/echopype/__init__.py", "/echopype/visualize/__init__.py", "/echopype/testing.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/echodata/__init__.py", "/echopype/visualize/api.py"], "/echopype/calibrate/env_params.py": ["/echopype/echodata/__init__.py", "/echopype/calibrate/cal_params.py"], "/echopype/convert/utils/ek_raw_parsers.py": ["/echopype/utils/log.py", "/echopype/convert/utils/ek_date_conversion.py"], "/echopype/tests/convert/test_convert_azfp.py": ["/echopype/__init__.py"], "/echopype/tests/calibrate/test_calibrate.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py"], "/echopype/calibrate/range.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/calibrate/env_params.py"], "/echopype/tests/calibrate/test_env_params.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params.py"], "/echopype/convert/set_groups_base.py": ["/echopype/echodata/convention/__init__.py", "/echopype/utils/coding.py", "/echopype/utils/prov.py"], "/echopype/clean/api.py": ["/echopype/utils/prov.py", "/echopype/clean/noise_est.py"], "/echopype/calibrate/calibrate_ek.py": ["/echopype/echodata/__init__.py", "/echopype/echodata/simrad.py", "/echopype/utils/log.py", "/echopype/calibrate/cal_params.py", "/echopype/calibrate/calibrate_base.py", "/echopype/calibrate/ecs.py", "/echopype/calibrate/ek80_complex.py", "/echopype/calibrate/env_params.py", "/echopype/calibrate/range.py"], "/echopype/calibrate/calibrate_base.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py", "/echopype/calibrate/ecs.py"], "/echopype/echodata/echodata.py": ["/echopype/utils/coding.py", "/echopype/utils/io.py", "/echopype/utils/log.py", "/echopype/utils/prov.py", "/echopype/echodata/convention/__init__.py", "/echopype/echodata/widgets/utils.py", "/echopype/echodata/widgets/widgets.py", "/echopype/core.py", "/echopype/convert/api.py"], "/echopype/visualize/api.py": ["/echopype/visualize/plot.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py", "/echopype/calibrate/calibrate_azfp.py", "/echopype/utils/log.py"], "/echopype/echodata/sensor_ep_version_mapping/v05x_to_v06x.py": ["/echopype/core.py", "/echopype/utils/log.py", "/echopype/echodata/convention/__init__.py"], "/echopype/clean/__init__.py": ["/echopype/clean/api.py"], "/echopype/visualize/plot.py": ["/echopype/visualize/cm.py", "/echopype/utils/log.py"], "/echopype/commongrid/__init__.py": ["/echopype/commongrid/api.py"], "/echopype/convert/parsed_to_zarr.py": ["/echopype/utils/io.py"], "/echopype/calibrate/__init__.py": ["/echopype/calibrate/api.py"], "/echopype/echodata/convention/utils.py": ["/echopype/echodata/convention/__init__.py"], "/echopype/tests/echodata/test_echodata.py": ["/echopype/__init__.py", "/echopype/calibrate/env_params_old.py", "/echopype/echodata/__init__.py", "/echopype/calibrate/calibrate_ek.py"], "/echopype/qc/api.py": ["/echopype/echodata/__init__.py", "/echopype/utils/log.py"], "/echopype/visualize/__init__.py": ["/echopype/visualize/api.py"], "/echopype/tests/qc/test_qc.py": ["/echopype/qc/__init__.py", "/echopype/qc/api.py"], "/echopype/core.py": ["/echopype/convert/parse_ad2cp.py", "/echopype/convert/parse_azfp.py", "/echopype/convert/parse_ek60.py", "/echopype/convert/parse_ek80.py", "/echopype/convert/parsed_to_zarr_ek60.py", "/echopype/convert/parsed_to_zarr_ek80.py", "/echopype/convert/set_groups_ad2cp.py", "/echopype/convert/set_groups_azfp.py", "/echopype/convert/set_groups_ek60.py", "/echopype/convert/set_groups_ek80.py"], "/echopype/convert/parse_ek60.py": ["/echopype/convert/parse_base.py"], "/echopype/tests/calibrate/test_cal_params.py": ["/echopype/calibrate/cal_params.py"], "/echopype/tests/calibrate/test_ecs.py": ["/echopype/calibrate/ecs.py"], "/echopype/echodata/__init__.py": ["/echopype/echodata/echodata.py"], "/echopype/tests/convert/test_convert_ek80.py": ["/echopype/__init__.py", "/echopype/testing.py", 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