afscgap 0.0.8

Tools for interacting with the public bottom trawl surveys data from the NOAA AFSC GAP.

Python Tools for AFSC GAP

Python-based tool chain ("Pyafscgap.org") for working with the public bottom trawl surveys data from the NOAA AFSC GAP. This provides information about where certain species were seen and when under what conditions, information useful for research in ocean health.

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Publication

See webpage, project Github, and example notebook.

Quickstart

You don't need any local software to get started! To learn about the dataset, explore a visual analytics app at https://app.pyafscgap.org (no code needed!). When ready, learn how to develop with these tools in a free hosted notebook tutorial.

Installation

Ready to take it to your own machine? Install the open source tools for accessing the AFSC GAP via Pypi / Pip:

$ pip install afscgap

The library's only dependency is requests and Pandas / numpy are not expected but supported.

Purpose

Unofficial Python-based tool set for interacting with bottom trawl surveys from the Ground Fish Assessment Program (GAP). It offers:

• Pythonic access to the official NOAA AFSC GAP API service.
• Tools for inference of the "negative" observations not provided by the API service.
• Visualization tools for quickly exploring and creating comparisons within the dataset, including for audiences with limited programming experience.

Note that GAP is an excellent dataset produced by the Resource Assessment and Conservation Engineering (RACE) Division of the Alaska Fisheries Science Center (AFSC) as part of the National Oceanic and Atmospheric Administration's Fisheries organization (NOAA Fisheries).

Needs

Scientists and developers working on ocean health have an interest in survey data from organizations like NOAA Fisheries. However,

• Using the GAP API from NOAA AFSC in Python can sometimes require a lot of work: understanding a complex schema, determining how to interact with a proprietary REST data service, forming long query URLs, and navigating pagination.
• The official API service provides presence-only data, complicating some common types of analysis and aggregation.
• Limited public tooling exists for visualizing within and, especially, creating comparisons between subsets of the AFSC GAP dataset which are useful for some types of investigation.

These various elements together may increase the barrier for working with these data, limiting their reach within some communities including the Python community.

Goals

This tool set aims to provide the following from the start to finish of an investigation:

• Visual analytics: Visualization tools for quickly exploring AFSC GAP, helping users start their investigations by finding and comparing subsets of interest within the broader dataset.
• API access: A type-annotated and documented Python interface to the official API service with ability to query with automated pagination, providing results in various formats compatible with different Python usage modalities (Pandas, pure-Python, etc). It adapts the HTTP-based API used by the agency with Python type hints for easy query and interface.
• Contextual documentation: Python docstrings annotate the data structures provided by the API to help users navigate the various fields available, offering contextual documentation when supported by Python IDEs.
• Absence inference: Tools to infer absence or "zero catch" data as required for certain analysis and aggregation using a supplemental hauls flat file dataset. Note that this flat file is provided by and hosted for this library's community after being created from non-API public AFSC GAP data. It is updated yearly.
• Query generation: This library converts more common Python standard types to types usable by the API service and emulated in Python when needed, reducing the need to interact directly with ORDS syntax.
• Accelerate specialized analysis: Affordances in code and non-code tools for both programmers and non-programmers to continue their investigation easily, including in tools outside this tool set.
• Inclusive design: Users of any skillset should be able to get something from this project.

Though not intended to be general, this project also provides an example for working with Oracle REST Data Services (ORDS) APIs in Python.

Usage

This library provides access to the AFSC GAP data with optional zero catch ("absence") record inference.

Visual analytics

Visualization tools are available to help both programmers and non-programmers start their investigation, providing a UI that stands on the other functionality provided by this project. This is available online at https://app.pyafscgap.org. It can generate both CSV (spreadsheet) exports and Python query code to move investigations to their next steps. To self-host or run this tool locally, see the visualization readme.

Basic queries

The afscgap.Query object is the main entry point into Python-based utilization. Calls can be written manually or generated in the visual analytics tool. For example, this requests all records of Pasiphaea pacifica in 2021 from the Gulf of Alaska to get the median bottom temperature when they were observed:

import statistics

import afscgap

# Build query
query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Get temperatures in Celsius
temperatures = [record.get_bottom_temperature(units='c') for record in results]

# Take the median
print(statistics.median(temperatures))

Note that afscgap.Query.execute returns a Cursor. One can iterate over this Cursor to access Record objects. You can do this with list comprehensions, maps, etc or with a good old for loop like in this example which gets a histogram of haul temperatures:

# Mapping from temperature in Celsius to count
count_by_temperature_c = {}

# Build query
query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Iterate through results and count
for record in results:
    temp = record.get_bottom_temperature(units='c')
    temp_rounded = round(temp)
    count = count_by_temperature_c.get(temp_rounded, 0) + 1
    count_by_temperature_c[temp_rounded] = count

# Print the result
print(count_by_temperature_c)

See data structure section. Using an iterator will have the library negotiate pagination behind the scenes so this operation will cause multiple HTTP requests while the iterator runs.

Enable absence data

One of the major limitations of the official API is that it only provides presence data. However, this library can optionally infer absence or "zero catch" records using a separate static file produced by NOAA AFSC GAP. The algorithm and details for absence inference is further discussed below.

Absence data / "zero catch" records inference can be turned on by passing False to set_presence_only in Query. To demonstrate, this example finds total area swept and total weight for Gadus macrocephalus from the Aleutian Islands in 2021:

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Gadus macrocephalus')
query.set_presence_only(False)
results = query.execute()

total_area = 0
total_weight = 0

for record in results:
    total_area += record.get_area_swept(units='ha')
    total_weight += record.get_weight(units='kg')

template = '%.2f kg / hectare swept (%.1f kg, %.1f hectares'
weight_per_area = total_weight / total_area
message = template % (weight_per_area, total_weight, total_area)

print(message)

For more details on the zero catch record feature, please see below.

Chaining

It is possible to use the Query object for method chaining.

import statistics

import afscgap

# Build query
results = afscgap.Query() \
    .filter_year(eq=2021) \
    .filter_srvy(eq='GOA') \
    .filter_scientific_name(eq='Pasiphaea pacifica') \
    .execute()

# Get temperatures in Celsius
temperatures = [record.get_bottom_temperature(units='c') for record in results]

# Take the median
print(statistics.median(temperatures))

Each filter and set method on Query returns the same query object.

Builder operations

Note that Query is a builder. So, it may be used to execute a search and then execute another search with slightly modified parameters:

import statistics

import afscgap

# Build query
query = afscgap.Query()
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')

# Get temperatures in Celsius for 2021
query.filter_year(eq=2021)
results = query.execute()
temperatures = [record.get_bottom_temperature(units='c') for record in results]
print(statistics.median(temperatures))

# Get temperatures in Celsius for 2019
query.filter_year(eq=2019)
results = query.execute()
temperatures = [record.get_bottom_temperature(units='c') for record in results]
print(statistics.median(temperatures))

When calling filter, all prior filters on the query object for that field are overwritten.

Serialization

Users may request a dictionary representation:

import afscgap

# Create a query
query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Get dictionary from individual record
for record in results:
    dict_representation = record.to_dict()
    print(dict_representation['bottom_temperature_c'])

# Execute again
results = query.execute()

# Get dictionary for all records
results_dicts = results.to_dicts()

for record in results_dicts:
    print(record['bottom_temperature_c'])

Note to_dicts returns an iterator by default, but it can be realized as a full list using the list() command.

Pandas

The dictionary form of the data can be used to create a Pandas dataframe:

import pandas

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

pandas.DataFrame(results.to_dicts())

Note that Pandas is not required to use this library.

Advanced filtering

You can provide range queries which translate to ORDS or Python emulated filters. For example, the following requests before and including 2019:

import afscgap

# Build query
query = afscgap.Query()
query.filter_year(max_val=2021)  # Note max_val
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Sum weight
weights = map(lambda x: x.get_weight(units='kg'), results)
total_weight = sum(weights)
print(total_weight)

The following requests data after and including 2019:

import afscgap

# Build query
query = afscgap.Query()
query.filter_year(min_val=2021)  # Note min_val
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Sum weight
weights = map(lambda x: x.get_weight(units='kg'), results)
total_weight = sum(weights)
print(total_weight)

Finally, the following requests data between 2015 and 2019 (includes 2015 and 2019):

import afscgap

# Build query
query = afscgap.Query()
query.filter_year(min_val=2015, max_val=2019)   # Note min/max_val
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

# Sum weight
weights = map(lambda x: x.get_weight(units='kg'), results)
total_weight = sum(weights)
print(total_weight)

For more advanced filters, please see manual filtering below.

Manual filtering

Users may provide advanced queries using Oracle's REST API query parameters. For example, this queries for 2021 records with haul from the Gulf of Alaska in a specific geographic area:

import afscgap

# Query with ORDS syntax
query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_latitude({'$between': [56, 57]})
query.filter_longitude({'$between': [-161, -160]})
results = query.execute()

# Summarize
count_by_common_name = {}

for record in results:
    common_name = record.get_common_name()
    new_count = record.get_count()
    count = count_by_common_name.get(common_name, 0) + new_count
    count_by_common_name[common_name] = count

# Print
print(count_by_common_name['walleye pollock'])

For more info about the options available, consider the Oracle docs or a helpful unaffiliated getting started tutorial from Jeff Smith.

Manual pagination

By default, the library will iterate through all results and handle pagination behind the scenes. However, one can also request an individual page:

import afscgap 

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Gadus macrocephalus')
results = query.execute()

results_for_page = results.get_page(offset=20, limit=53)
print(len(results_for_page))

Client code can also change the pagination behavior used when iterating:

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Gadus macrocephalus')
query.set_start_offset(10)
query.set_limit(200)
query.set_filter_incomplete(True)
results = query.execute()

for record in results:
    print(record.get_bottom_temperature(units='c'))

Note that records are only requested once during iteration and only after the prior page has been returned via the iterator ("lazy" loading).

Data structure

The schema drives the getters and filters available on in the library. Note that data structures are defined in the model submodule but client code generally only needs to interact with Record objects.

Schema

A Python-typed description of the fields is provided below.

Field	Python Type	Description
year	float	Year for the survey in which this observation was made.
srvy	str	The name of the survey in which this observation was made. NBS (N Bearing Sea), EBS (SE Bearing Sea), BSS (Bearing Sea Slope), or GOA (Gulf of Alaska)
survey	str	Long form description of the survey in which the observation was made.
survey_id	float	Unique numeric ID for the survey.
cruise	float	An ID uniquely identifying the cruise in which the observation was made. Multiple cruises in a survey.
haul	float	An ID uniquely identifying the haul in which this observation was made. Multiple hauls per cruise.
stratum	float	Unique ID for statistical area / survey combination as described in the metadata or 0 if an experimental tow.
station	str	Station associated with the survey.
vessel_name	str	Unique ID describing the vessel that made this observation. This is left as a string but, in practice, is likely numeric.
vessel_id	float	Name of the vessel at the time the observation was made with multiple names potentially associated with a vessel ID.
date_time	str	The date and time of the haul which has been attempted to be transformed to an ISO 8601 string without timezone info. If it couldn’t be transformed, the original string is reported.
latitude_dd	float	Latitude in decimal degrees associated with the haul.
longitude_dd	float	Longitude in decimal degrees associated with the haul.
species_code	float	Unique ID associated with the species observed.
common_name	str	The “common name” associated with the species observed. Example: Pacific glass shrimp
scientific_name	str	The “scientific name” associated with the species observed. Example: Pasiphaea pacifica
taxon_confidence	str	Confidence flag regarding ability to identify species (High, Moderate, Low). In practice, this can also be Unassessed.
cpue_kgha	Optional[float]	Catch weight divided by net area (kg / hectares) if available. See metadata. None if could not interpret as a float.
cpue_kgkm2	Optional[float]	Catch weight divided by net area (kg / km^2) if available. See metadata. None if could not interpret as a float.
cpue_kg1000km2	Optional[float]	Catch weight divided by net area (kg / km^2 * 1000) if available. See metadata. None if could not interpret as a float.
cpue_noha	Optional[float]	Catch number divided by net sweep area if available (count / hectares). See metadata. None if could not interpret as a float.
cpue_nokm2	Optional[float]	Catch number divided by net sweep area if available (count / km^2). See metadata. None if could not interpret as a float.
cpue_no1000km2	Optional[float]	Catch number divided by net sweep area if available (count / km^2 * 1000). See metadata. None if could not interpret as a float.
weight_kg	Optional[float]	Taxon weight (kg) if available. See metadata. None if could not interpret as a float.
count	Optional[float]	Total number of organism individuals in haul. None if could not interpret as a float.
bottom_temperature_c	Optional[float]	Bottom temperature associated with observation if available in Celsius. None if not given or could not interpret as a float.
surface_temperature_c	Optional[float]	Surface temperature associated with observation if available in Celsius. None if not given or could not interpret as a float.
depth_m	float	Depth of the bottom in meters.
distance_fished_km	float	Distance of the net fished as km.
net_width_m	float	Distance of the net fished as m.
net_height_m	float	Height of the net fished as m.
area_swept_ha	float	Area covered by the net while fishing in hectares.
duration_hr	float	Duration of the haul as number of hours.
tsn	Optional[int]	Taxonomic information system species code.
ak_survey_id	int	AK identifier for the survey.

For more information on the schema, see the metadata repository but note that the fields may be slightly different in the Python library per what is actually returned by the API.

Filters and getters

These fields are available as getters on afscgap.model.Record (result.get_srvy()) and may be used as optional filters on the query asfcgagp.query(srvy='GOA'). Fields which are Optional have two getters. First, the "regular" getter (result.get_count()) will assert that the field is not None before returning a non-optional. The second "maybe" getter (result.get_count_maybe()) will return None if the value was not provided or could not be parsed.

API Field	Filter on Query	Regular Getter	Maybe Getter
year	filter_year()	get_year() -> float
srvy	filter_srvy()	get_srvy() -> str
survey	filter_survey()	get_survey() -> str
survey_id	filter_survey_id()	get_survey_id() -> float
cruise	filter_cruise()	get_cruise() -> float
haul	filter_haul()	get_haul() -> float
stratum	filter_stratum()	get_stratum() -> float
station	filter_station()	get_station() -> str
vessel_name	filter_vessel_name()	get_vessel_name() -> str
vessel_id	filter_vessel_id()	get_vessel_id() -> float
date_time	filter_date_time()	get_date_time() -> str
latitude_dd	filter_latitude(units='dd')	get_latitude(units='dd') -> float
longitude_dd	filter_longitude(units='dd')	get_longitude(units='dd') -> float
species_code	filter_species_code()	get_species_code() -> float
common_name	filter_common_name()	get_common_name() -> str
scientific_name	filter_scientific_name()	get_scientific_name() -> str
taxon_confidence	filter_taxon_confidence()	get_taxon_confidence() -> str
cpue_kgha	filter_cpue_weight(units='kg/ha')	get_cpue_weight(units='kg/ha') -> float	get_cpue_weight_maybe(units='kg/ha') -> Optional[float]
cpue_kgkm2	filter_cpue_weight(units='kg/km2')	get_cpue_weight(units='kg/km2') -> float	get_cpue_weight_maybe(units='kg/km2') -> Optional[float]
cpue_kg1000km2	filter_cpue_weight(units='kg1000/km2')	get_cpue_weight(units='kg1000/km2') -> float	get_cpue_weight_maybe(units='kg1000/km2') -> Optional[float]
cpue_noha	filter_cpue_count(units='count/ha')	get_cpue_count(units='count/ha') -> float	get_cpue_count_maybe(units='count/ha') -> Optional[float]
cpue_nokm2	filter_cpue_count(units='count/km2')	get_cpue_count(units='count/km2') -> float	get_cpue_count_maybe(units='count/km2') -> Optional[float]
cpue_no1000km2	filter_cpue_count(units='count1000/km2')	get_cpue_count(units='count1000/km2') -> float	get_cpue_count_maybe(units='count1000/km2') -> Optional[float]
weight_kg	filter_weight(units='kg')	get_weight(units='kg') -> float	get_weight_maybe() -> Optional[float]
count	filter_count()	get_count() -> float	get_count_maybe() -> Optional[float]
bottom_temperature_c	filter_bottom_temperature(units='c')	get_bottom_temperature(units='c') -> float	get_bottom_temperature_maybe(units='c') -> Optional[float]
surface_temperature_c	filter_surface_temperature(units='c')	get_surface_temperature(units='c') -> float	get_surface_temperature_maybe() -> Optional[float]
depth_m	filter_depth(units='m')	get_depth(units='m') -> float
distance_fished_km	filter_distance_fished(units='km')	get_distance_fished(units='km') -> float
net_width_m	filter_net_width(units='m')	get_net_width(units='m') -> float	get_net_width(units='m') -> Optional[float]
net_height_m	filter_net_height(units='m')	get_net_height(units='m') -> float	get_net_height(units='m') -> Optional[float]
area_swept_ha	filter_area_swept(units='ha')	get_area_swept(units='ha') -> float
duration_hr	filter_duration(units='hr')	get_duration(units='hr') -> float
tsn	filter_tsn()	get_tsn() -> int	get_tsn_maybe() -> Optional[int]
ak_survey_id	filter_ak_survey_id()	get_ak_survey_id() -> int

Support for additional units are available for some fields and are calculated on the fly within the afscgap library when requested. Record objects also have a is_complete method which returns true if all the fields with an Optional type are non-None and the date_time could be parsed and made into an ISO 8601 string.

Absence vs presence data

The API itself provides access to presence only data. This means that records are only given for when a species was found. This can cause issues if trying to aggregate data like, for example, to determine the weight of the species in a region in terms of catch weight per hectare. The AFSC GAP API on its own would not necessarily provide the total nubmer of hecatres surveyed in that region because hauls without the species present would be excluded. That in mind, this library provides a method for inferring absence data.

Example of absence data in aggregation

Here is a practical memory efficient example using geolib and toolz to aggregate catch data by 5 character geohash.

import afscgap
import geolib.geohash
import toolz.itertoolz

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Gadus macrocephalus')
query.set_presence_only(False)
results = query.execute()

def simplify_record(full_record):
    latitude = full_record.get_latitude(units='dd')
    longitude = full_record.get_longitude(units='dd')
    geohash = geolib.geohash.encode(latitude, longitude, 5)
    
    return {
        'geohash': geohash,
        'area': full_record.get_area_swept(units='ha'),
        'weight': full_record.get_weight(units='kg')
    }

def combine_record(a, b):
    assert a['geohash'] == b['geohash']
    return {
        'geohash': a['geohash'],
        'area': a['area'] + b['area'],
        'weight': a['weight'] + b['weight']
    }

simplified_records = map(simplify_record, results)
totals_by_geohash = toolz.reduceby(
    'geohash',
    combine_record,
    simplified_records
)
weight_by_area_tuples = map(
    lambda x: (x['geohash'], x['weight'] / x['area']),
    totals_by_geohash.values()
)
weight_by_area_by_geohash = dict(weight_by_area_tuples)

For more details see the Python functional programming guide. All that said, for some queries, the use of Pandas may lead to very heavy memory usage.

Absence inference algorithm

Though it is not possible to resolve this issue using the AFSC GAP API service alone, this library can infer those missing records using a separate static flat file provided by NOAA and the following algorithm:

• Record the set of species observed from API service returned results.
• Record the set of hauls observed from API service returned results.
• Return records normally while records remain available from the API service.
• Upon exhaustion of the API service results, download the ~10M hauls flat file from this library's community.
• For each species observed in the API returned results, check if that species had a record for each haul reported in the flat file.
• For any hauls without the species record, yield an 0 catch record from the iterator for that query.

This procedure is disabled by default. However, it can be enabled through the presence_only keyword in query like so: asfcgap.query(presence_only=False).

Memory efficiency of absence inference

Note that presence_only=False will return a lot of records. Indeed, in some queries, this may stretch to many millions. As described in community guidelines, a goal of this project is provide those data in a memory-efficient way and, specifically, these "zero catch" records are generated by the library's iterator as requested but never all held in memory at the same time. It is recommened that client code also take care in memory efficiency. This can be as simple as aggregating via for loops which only hold one record in memory at a time. Similarly, consider using map, filter, reduce, itertools, etc.

Manual pagination of zero catch records

The goal of Cursor.get_page is to pull results from a page returned for a query as it appears in the NOAA API service. Note that get_page will not return zero catch records even with presence_only=False because the "page" requested does not technically exist in the API service. In order to use the negative records inference feature, please use the iterator option instead.

Filtering absence data

Note that the library will emulate filtering in Python so that haul records are filtered just as presence records are filtered by the API service. This works for "basic" and "advanced" filtering. However, at time of writing, "manual filtering" as described below using ORDS syntax is not supported when presence_data=False. Also, by default, a warning will be emitted when using this feature to help new users be aware of potential memory issues. This can be suppressed by including suppress_large_warning=True in the call to query.

Cached hauls

If desired, a cached set of hauls data can be used instead. It must be a list of Haul objects and can be passed like so:

import csv

import afscgap
import afscgap.inference

with open('hauls.csv') as f:
    rows = csv.DictReader(f)
    hauls = [afscgap.inference.parse_haul(row) for row in rows]

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Gadus macrocephalus')
query.set_presence_only(False)
query.set_hauls_prefetch(hauls)
results = query.execute()

This can be helpful when executing a lot of queries and the bandwidth to download the hauls metadata file multiple times may not be desireable.

Data quality and completeness

There are a few caveats for working with these data that are important for researchers to understand.

Incomplete or invalid records

Metadata fields such as year are always required to make a Record whereas others such as catch weight cpue_kgkm2 are not present on all records returned by the API and are optional. See the data structure section for additional details. For fields with optional values:

• A maybe getter (like get_cpue_weight_maybe) is provided which will return None without error if the value is not provided or could not be parsed.
• A regular getter (like get_cpue_weight) is provided which asserts the value is not None before it is returned.

Record objects also have an is_complete method which returns true if both all optional fields on the Record are non-None and the date_time field on the Record is a valid ISO 8601 string. By default, records for which is_complete are false are returned when iterating or through get_page but this can be overridden by with the filter_incomplete keyword argument like so:

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
query.set_filter_incomplete(True)
results = query.execute()

for result in results:
    assert result.is_complete()

Results returned by the API for which non-Optional fields could not be parsed (like missing year) are considered "invalid" and always excluded during iteration when those raw unreadable records are kept in a queue.Queue[dict] that can be accessed via get_invalid like so:

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_srvy(eq='GOA')
query.filter_scientific_name(eq='Pasiphaea pacifica')
results = query.execute()

valid = list(results)

invalid_queue = results.get_invalid()
percent_invalid = invalid_queue.qsize() / len(valid) * 100
print('Percent invalid: %%%.2f' % percent_invalid)

complete = filter(lambda x: x.is_complete(), valid)
num_complete = sum(map(lambda x: 1, complete))
percent_complete = num_complete / len(valid) * 100
print('Percent complete: %%%.2f' % percent_complete)

Note that this queue is filled during iteration (like for result in results or list(results)) and not get_page whose invalid record handling behavior can be specified via the ignore_invalid keyword.

Longitude

Though not officially mentioned by the NOAA API, the authors of this library observe some positive longitudes in returned data where negative longitudes of the same magnitude would be expected. Users of the library should be careful to determine how to handle these records (inferring they should have been the same magnitude of longitude but negative or excluded). Publications should be careful to document their decision.

License

We are happy to make this library available under the BSD 3-Clause license. See LICENSE for more details. (c) 2023 Regents of University of California. See the Eric and Wendy Schmidt Center for Data Science and the Environment at UC Berkeley.

Local development

After installing dev dependencies (pip install -e .[dev]), we recommend the following local checks:

$ nose2 --start-dir=afscgap
$ mypy afscgap/*.py
$ pyflakes afscgap/*.py
$ pycodestyle afscgap/*.py

Note these checks are run by CI / CD. Also, afscgapviz has separate tests as described in the visualization readme.

Community

Thanks for your support! Pull requests and issues very welcome.

Contribution guidelines

We invite contributions via our project Github. Please read the CONTRIBUTING.md file for more information.

Debugging

While participating in the community, you may need to debug URL generation. Therefore, for investigating issues or evaluating the underlying operations, you can also request a full URL for a query:

import afscgap

query = afscgap.Query()
query.filter_year(eq=2021)
query.filter_latitude(eq={'$between': [56, 57]})
query.filter_longitude(eq={'$between': [-161, -160]})
results = query.execute()

print(results.get_page_url(limit=10, offset=0))

The query can be executed by making an HTTP GET request at the provided location.

People

Sam Pottinger is the primary contact with additional development from Giulia Zarpellon. Additionally some acknowledgements:

• Thank you to Carl Boettiger and Fernando Perez for advice in the Python library.
• Thanks also to Maya Weltman-Fahs, Brookie Guzder-Williams, and Magali de Bruyn for advice on the visual analytics tool.

This is a project of the The Eric and Wendy Schmidt Center for Data Science and the Environment at UC Berkeley where Kevin Koy is Executive Director. Please contact us via [e-mail].

Open Source

We are happy to be part of the open source community.

At this time, the only open source dependency used by this microlibrary is Requests which is available under the Apache v2 License from Kenneth Reitz and other contributors.

In addition to Github-provided Github Actions, our build and documentation systems also use the following but are not distributed with or linked to the project itself:

• mypy under the MIT License from Jukka Lehtosalo, Dropbox, and other contributors.
• nose2 under a BSD license from Jason Pellerin and other contributors.
• pdoc under the Unlicense license from Andrew Gallant and Maximilian Hils.
• pycodestyle under the Expat License from Johann C. Rocholl, Florent Xicluna, and Ian Lee.
• pyflakes under the MIT License from Divmod, Florent Xicluna, and other contributors.
• sftp-action under the MIT License from Niklas Creepios.
• ssh-action under the MIT License from Bo-Yi Wu.

Next, the visualization tool has additional dependencies as documented in the visualization readme.

Finally, note that the website uses assets from The Noun Project under the NounPro plan. If used outside of https://pyafscgap.org, they may be subject to a different license.

Thank you to all of these projects for their contribution.

Version history

Annotated version history:

• 0.0.7: Visual analytics tools.
• 0.0.6: Performance and size improvements.
• 0.0.5: Changes to documentation.
• 0.0.4: Negative / zero catch inference.
• 0.0.3: Minor updates in documentation.
• 0.0.2: License under BSD.
• 0.0.1: Initial release.