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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 The Boston Light Swim temperature analysis with Python
In the past we demonstrated how to perform a CSW catalog search with OWSLib,
and how to obtain near real-time data with pyoos.
In this notebook we will use both to find all observations and model data around the Boston Harbor to access the sea water temperature.
This workflow is part of an example to advise swimmers of the annual Boston lighthouse swim of the Boston Harbor water temperature conditions prior to the race. For more information regarding the workflow presented here see Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle.   2016. "Dynamic Reusable Workflows for Ocean Science." J. Mar. Sci. Eng. 4, no. 4
Step1:  This notebook is quite big and complex,
so to help us keep things organized we'll define a cell with the most important options and switches.
Below we can define the date,
bounding box, phenomena SOS and CF names and units,
and the catalogs we will search.
Step2:  We'll print some of the search configuration options along the way to keep track of them.
Step3:  We already created an OWSLib.fes filter before.
The main difference here is that we do not want the atmosphere model data,
so we are filtering out all the GRIB-2 data format.
Step4:  In the cell below we ask the catalog for all the returns that match the filter and have an OPeNDAP endpoint.
Step5:  We found some models, and observations from NERACOOS there.
However, we do know that there are some buoys from NDBC and CO-OPS available too.
Also, those NERACOOS observations seem to be from a CTD mounted at 65 meters below the sea surface. Rendering them useless from our purpose.
So let's use the catalog only for the models by filtering the observations with is_station below.
And we'll rely CO-OPS and NDBC services for the observations.
Step6:  Now we can use pyoos collectors for NdbcSos,
Step7:  and CoopsSos.
Step8:  We will join all the observations into an uniform series, interpolated to 1-hour interval, for the model-data comparison.
This step is necessary because the observations can be 7 or 10 minutes resolution,
while the models can be 30 to 60 minutes.
Step9:  In this next cell we will save the data for quicker access later.
Step10:  Taking a quick look at the observations
Step11:  Now it is time to loop the models we found above,
Step12:  Next, we will match them with the nearest observed time-series. The max_dist=0.08 is in degrees, that is roughly 8 kilometers.
Step13:  Now it is possible to compute some simple comparison metrics. First we'll calculate the model mean bias
Step14:  And the root mean squared rrror of the deviations from the mean
Step15:  The next 2 cells make the scores "pretty" for plotting.
Step16:  The cells from [20] to [25] create a folium map with bokeh for the time-series at the observed points.
Note that we did mark the nearest model cell location used in the comparison.
Step17:  Here we use a dictionary with some models we expect to find so we can create a better legend for the plots. If any new models are found, we will use its filename in the legend as a default until we can go back and add a short name to our library.
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Python Code:
import warnings
# Suppresing warnings for a "pretty output."
warnings.simplefilter("ignore")
Explanation: The Boston Light Swim temperature analysis with Python
In the past we demonstrated how to perform a CSW catalog search with OWSLib,
and how to obtain near real-time data with pyoos.
In this notebook we will use both to find all observations and model data around the Boston Harbor to access the sea water temperature.
This workflow is part of an example to advise swimmers of the annual Boston lighthouse swim of the Boston Harbor water temperature conditions prior to the race. For more information regarding the workflow presented here see Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle.   2016. "Dynamic Reusable Workflows for Ocean Science." J. Mar. Sci. Eng. 4, no. 4: 68.
End of explanation
%%writefile config.yaml
# Specify a YYYY-MM-DD hh:mm:ss date or integer day offset.
# If both start and stop are offsets they will be computed relative to datetime.today() at midnight.
# Use the dates commented below to reproduce the last Boston Light Swim event forecast.
date:
    start: -5 # 2016-8-16 00:00:00
    stop: +4 # 2016-8-29 00:00:00
run_name: 'latest'
# Boston harbor.
region:
    bbox: [-71.3, 42.03, -70.57, 42.63]
    # Try the bounding box below to see how the notebook will behave for a different region.
    #bbox: [-74.5, 40, -72., 41.5]
    crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'
sos_name: 'sea_water_temperature'
cf_names:
    - sea_water_temperature
    - sea_surface_temperature
    - sea_water_potential_temperature
    - equivalent_potential_temperature
    - sea_water_conservative_temperature
    - pseudo_equivalent_potential_temperature
units: 'celsius'
catalogs:
    - https://data.ioos.us/csw
Explanation: This notebook is quite big and complex,
so to help us keep things organized we'll define a cell with the most important options and switches.
Below we can define the date,
bounding box, phenomena SOS and CF names and units,
and the catalogs we will search.
End of explanation
import os
import shutil
from datetime import datetime
from ioos_tools.ioos import parse_config
config = parse_config("config.yaml")
# Saves downloaded data into a temporary directory.
save_dir = os.path.abspath(config["run_name"])
if os.path.exists(save_dir):
    shutil.rmtree(save_dir)
os.makedirs(save_dir)
fmt = "{:*^64}".format
print(fmt("Saving data inside directory {}".format(save_dir)))
print(fmt(" Run information "))
print("Run date: {:%Y-%m-%d %H:%M:%S}".format(datetime.utcnow()))
print("Start: {:%Y-%m-%d %H:%M:%S}".format(config["date"]["start"]))
print("Stop: {:%Y-%m-%d %H:%M:%S}".format(config["date"]["stop"]))
print(
    "Bounding box: {0:3.2f}, {1:3.2f},"
    "{2:3.2f}, {3:3.2f}".format(*config["region"]["bbox"])
)
Explanation: We'll print some of the search configuration options along the way to keep track of them.
End of explanation
def make_filter(config):
    from owslib import fes
    from ioos_tools.ioos import fes_date_filter
    kw = dict(
        wildCard="*", escapeChar="\\", singleChar="?", propertyname="apiso:AnyText"
    )
    or_filt = fes.Or(
        [fes.PropertyIsLike(literal=("*%s*" % val), **kw) for val in config["cf_names"]]
    )
    not_filt = fes.Not([fes.PropertyIsLike(literal="GRIB-2", **kw)])
    begin, end = fes_date_filter(config["date"]["start"], config["date"]["stop"])
    bbox_crs = fes.BBox(config["region"]["bbox"], crs=config["region"]["crs"])
    filter_list = [fes.And([bbox_crs, begin, end, or_filt, not_filt])]
    return filter_list
filter_list = make_filter(config)
Explanation: We already created an OWSLib.fes filter before.
The main difference here is that we do not want the atmosphere model data,
so we are filtering out all the GRIB-2 data format.
End of explanation
from ioos_tools.ioos import get_csw_records, service_urls
from owslib.csw import CatalogueServiceWeb
dap_urls = []
print(fmt(" Catalog information "))
for endpoint in config["catalogs"]:
    print("URL: {}".format(endpoint))
    try:
        csw = CatalogueServiceWeb(endpoint, timeout=120)
    except Exception as e:
        print("{}".format(e))
        continue
    csw = get_csw_records(csw, filter_list, esn="full")
    OPeNDAP = service_urls(csw.records, identifier="OPeNDAP:OPeNDAP")
    odp = service_urls(
        csw.records, identifier="urn:x-esri:specification:ServiceType:odp:url"
    )
    dap = OPeNDAP + odp
    dap_urls.extend(dap)
    print("Number of datasets available: {}".format(len(csw.records.keys())))
    for rec, item in csw.records.items():
        print("{}".format(item.title))
    if dap:
        print(fmt(" DAP "))
        for url in dap:
            print("{}.html".format(url))
    print("\n")
# Get only unique endpoints.
dap_urls = list(set(dap_urls))
Explanation: In the cell below we ask the catalog for all the returns that match the filter and have an OPeNDAP endpoint.
End of explanation
from ioos_tools.ioos import is_station
from timeout_decorator import TimeoutError
# Filter out some station endpoints.
non_stations = []
for url in dap_urls:
    url = f"{url}#fillmismatch"
    try:
        if not is_station(url):
            non_stations.append(url)
    except (IOError, OSError, RuntimeError, TimeoutError) as e:
        print("Could not access URL {}.html\n{!r}".format(url, e))
dap_urls = non_stations
print(fmt(" Filtered DAP "))
for url in dap_urls:
    print("{}.html".format(url))
Explanation: We found some models, and observations from NERACOOS there.
However, we do know that there are some buoys from NDBC and CO-OPS available too.
Also, those NERACOOS observations seem to be from a CTD mounted at 65 meters below the sea surface. Rendering them useless from our purpose.
So let's use the catalog only for the models by filtering the observations with is_station below.
And we'll rely CO-OPS and NDBC services for the observations.
End of explanation
from pyoos.collectors.ndbc.ndbc_sos import NdbcSos
collector_ndbc = NdbcSos()
collector_ndbc.set_bbox(config["region"]["bbox"])
collector_ndbc.end_time = config["date"]["stop"]
collector_ndbc.start_time = config["date"]["start"]
collector_ndbc.variables = [config["sos_name"]]
ofrs = collector_ndbc.server.offerings
title = collector_ndbc.server.identification.title
print(fmt(" NDBC Collector offerings "))
print("{}: {} offerings".format(title, len(ofrs)))
import pandas as pd
from ioos_tools.ioos import collector2table
ndbc = collector2table(
    collector=collector_ndbc, config=config, col="sea_water_temperature (C)"
)
if ndbc:
    data = dict(
        station_name=[s._metadata.get("station_name") for s in ndbc],
        station_code=[s._metadata.get("station_code") for s in ndbc],
        sensor=[s._metadata.get("sensor") for s in ndbc],
        lon=[s._metadata.get("lon") for s in ndbc],
        lat=[s._metadata.get("lat") for s in ndbc],
        depth=[s._metadata.get("depth") for s in ndbc],
    )
table = pd.DataFrame(data).set_index("station_code")
table
Explanation: Now we can use pyoos collectors for NdbcSos,
End of explanation
from pyoos.collectors.coops.coops_sos import CoopsSos
collector_coops = CoopsSos()
collector_coops.set_bbox(config["region"]["bbox"])
collector_coops.end_time = config["date"]["stop"]
collector_coops.start_time = config["date"]["start"]
collector_coops.variables = [config["sos_name"]]
ofrs = collector_coops.server.offerings
title = collector_coops.server.identification.title
print(fmt(" Collector offerings "))
print("{}: {} offerings".format(title, len(ofrs)))
coops = collector2table(
    collector=collector_coops, config=config, col="sea_water_temperature (C)"
)
if coops:
    data = dict(
        station_name=[s._metadata.get("station_name") for s in coops],
        station_code=[s._metadata.get("station_code") for s in coops],
        sensor=[s._metadata.get("sensor") for s in coops],
        lon=[s._metadata.get("lon") for s in coops],
        lat=[s._metadata.get("lat") for s in coops],
        depth=[s._metadata.get("depth") for s in coops],
    )
table = pd.DataFrame(data).set_index("station_code")
table
Explanation: and CoopsSos.
End of explanation
data = ndbc + coops
index = pd.date_range(
    start=config["date"]["start"].replace(tzinfo=None),
    end=config["date"]["stop"].replace(tzinfo=None),
    freq="1H",
)
# Preserve metadata with `reindex`.
observations = []
for series in data:
    _metadata = series._metadata
    series.index = series.index.tz_localize(None)
    series.index = series.index.tz_localize(None)
    obs = series.reindex(index=index, limit=1, method="nearest")
    obs._metadata = _metadata
    observations.append(obs)
Explanation: We will join all the observations into an uniform series, interpolated to 1-hour interval, for the model-data comparison.
This step is necessary because the observations can be 7 or 10 minutes resolution,
while the models can be 30 to 60 minutes.
End of explanation
import iris
from ioos_tools.tardis import series2cube
attr = dict(
    featureType="timeSeries",
    Conventions="CF-1.6",
    standard_name_vocabulary="CF-1.6",
    cdm_data_type="Station",
    comment="Data from http://opendap.co-ops.nos.noaa.gov",
)
cubes = iris.cube.CubeList([series2cube(obs, attr=attr) for obs in observations])
outfile = os.path.join(save_dir, "OBS_DATA.nc")
iris.save(cubes, outfile)
Explanation: In this next cell we will save the data for quicker access later.
End of explanation
%matplotlib inline
ax = pd.concat(data).plot(figsize=(11, 2.25))
Explanation: Taking a quick look at the observations:
End of explanation
from ioos_tools.ioos import get_model_name
from ioos_tools.tardis import get_surface, is_model, proc_cube, quick_load_cubes
from iris.exceptions import ConstraintMismatchError, CoordinateNotFoundError, MergeError
print(fmt(" Models "))
cubes = dict()
for k, url in enumerate(dap_urls):
    print("\n[Reading url {}/{}]: {}".format(k + 1, len(dap_urls), url))
    try:
        cube = quick_load_cubes(url, config["cf_names"], callback=None, strict=True)
        if is_model(cube):
            cube = proc_cube(
                cube,
                bbox=config["region"]["bbox"],
                time=(config["date"]["start"], config["date"]["stop"]),
                units=config["units"],
            )
        else:
            print("[Not model data]: {}".format(url))
            continue
        cube = get_surface(cube)
        mod_name = get_model_name(url)
        cubes.update({mod_name: cube})
    except (
        RuntimeError,
        ValueError,
        ConstraintMismatchError,
        CoordinateNotFoundError,
        IndexError,
    ) as e:
        print("Cannot get cube for: {}\n{}".format(url, e))
Explanation: Now it is time to loop the models we found above,
End of explanation
import iris
from ioos_tools.tardis import (
    add_station,
    ensure_timeseries,
    get_nearest_water,
    make_tree,
    remove_ssh,
)
from iris.pandas import as_series
for mod_name, cube in cubes.items():
    fname = "{}.nc".format(mod_name)
    fname = os.path.join(save_dir, fname)
    print(fmt(" Downloading to file {} ".format(fname)))
    try:
        tree, lon, lat = make_tree(cube)
    except CoordinateNotFoundError:
        print("Cannot make KDTree for: {}".format(mod_name))
        continue
    # Get model series at observed locations.
    raw_series = dict()
    for obs in observations:
        obs = obs._metadata
        station = obs["station_code"]
        try:
            kw = dict(k=10, max_dist=0.08, min_var=0.01)
            args = cube, tree, obs["lon"], obs["lat"]
            try:
                series, dist, idx = get_nearest_water(*args, **kw)
            except RuntimeError as e:
                print("Cannot download {!r}.\n{}".format(cube, e))
                series = None
        except ValueError:
            status = "No Data"
            print("[{}] {}".format(status, obs["station_name"]))
            continue
        if not series:
            status = "Land   "
        else:
            raw_series.update({station: series})
            series = as_series(series)
            status = "Water  "
        print("[{}] {}".format(status, obs["station_name"]))
    if raw_series:  # Save cube.
        for station, cube in raw_series.items():
            cube = add_station(cube, station)
            cube = remove_ssh(cube)
        try:
            cube = iris.cube.CubeList(raw_series.values()).merge_cube()
        except MergeError as e:
            print(e)
        ensure_timeseries(cube)
        try:
            iris.save(cube, fname)
        except AttributeError:
            # FIXME: we should patch the bad attribute instead of removing everything.
            cube.attributes = {}
            iris.save(cube, fname)
        del cube
    print("Finished processing [{}]".format(mod_name))
Explanation: Next, we will match them with the nearest observed time-series. The max_dist=0.08 is in degrees, that is roughly 8 kilometers.
End of explanation
from ioos_tools.ioos import stations_keys
def rename_cols(df, config):
    cols = stations_keys(config, key="station_name")
    return df.rename(columns=cols)
from ioos_tools.ioos import load_ncs
from ioos_tools.skill_score import apply_skill, mean_bias
dfs = load_ncs(config)
df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)
skill_score = dict(mean_bias=df.to_dict())
# Filter out stations with no valid comparison.
df.dropna(how="all", axis=1, inplace=True)
df = df.applymap("{:.2f}".format).replace("nan", "--")
Explanation: Now it is possible to compute some simple comparison metrics. First we'll calculate the model mean bias:
$$ \text{MB} = \mathbf{\overline{m}} - \mathbf{\overline{o}}$$
End of explanation
from ioos_tools.skill_score import rmse
dfs = load_ncs(config)
df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)
skill_score["rmse"] = df.to_dict()
# Filter out stations with no valid comparison.
df.dropna(how="all", axis=1, inplace=True)
df = df.applymap("{:.2f}".format).replace("nan", "--")
Explanation: And the root mean squared rrror of the deviations from the mean:
$$ \text{CRMS} = \sqrt{\left(\mathbf{m'} - \mathbf{o'}\right)^2}$$
where: $\mathbf{m'} = \mathbf{m} - \mathbf{\overline{m}}$ and $\mathbf{o'} = \mathbf{o} - \mathbf{\overline{o}}$
End of explanation
import pandas as pd
# Stringfy keys.
for key in skill_score.keys():
    skill_score[key] = {str(k): v for k, v in skill_score[key].items()}
mean_bias = pd.DataFrame.from_dict(skill_score["mean_bias"])
mean_bias = mean_bias.applymap("{:.2f}".format).replace("nan", "--")
skill_score = pd.DataFrame.from_dict(skill_score["rmse"])
skill_score = skill_score.applymap("{:.2f}".format).replace("nan", "--")
import folium
from ioos_tools.ioos import get_coordinates
def make_map(bbox, **kw):
    line = kw.pop("line", True)
    layers = kw.pop("layers", True)
    zoom_start = kw.pop("zoom_start", 5)
    lon = (bbox[0] + bbox[2]) / 2
    lat = (bbox[1] + bbox[3]) / 2
    m = folium.Map(
        width="100%", height="100%", location=[lat, lon], zoom_start=zoom_start
    )
    if layers:
        url = "http://oos.soest.hawaii.edu/thredds/wms/hioos/satellite/dhw_5km"
        w = folium.WmsTileLayer(
            url,
            name="Sea Surface Temperature",
            fmt="image/png",
            layers="CRW_SST",
            attr="PacIOOS TDS",
            overlay=True,
            transparent=True,
        )
        w.add_to(m)
    if line:
        p = folium.PolyLine(
            get_coordinates(bbox), color="#FF0000", weight=2, opacity=0.9,
        )
        p.add_to(m)
    return m
bbox = config["region"]["bbox"]
m = make_map(bbox, zoom_start=11, line=True, layers=True)
Explanation: The next 2 cells make the scores "pretty" for plotting.
End of explanation
all_obs = stations_keys(config)
from glob import glob
from operator import itemgetter
import iris
from folium.plugins import MarkerCluster
iris.FUTURE.netcdf_promote = True
big_list = []
for fname in glob(os.path.join(save_dir, "*.nc")):
    if "OBS_DATA" in fname:
        continue
    cube = iris.load_cube(fname)
    model = os.path.split(fname)[1].split("-")[-1].split(".")[0]
    lons = cube.coord(axis="X").points
    lats = cube.coord(axis="Y").points
    stations = cube.coord("station_code").points
    models = [model] * lons.size
    lista = zip(models, lons.tolist(), lats.tolist(), stations.tolist())
    big_list.extend(lista)
big_list.sort(key=itemgetter(3))
df = pd.DataFrame(big_list, columns=["name", "lon", "lat", "station"])
df.set_index("station", drop=True, inplace=True)
groups = df.groupby(df.index)
locations, popups = [], []
for station, info in groups:
    sta_name = all_obs[station]
    for lat, lon, name in zip(info.lat, info.lon, info.name):
        locations.append([lat, lon])
        popups.append(
            "[{}]: {}".format(name.rstrip("fillmismatch").rstrip("#"), sta_name)
        )
MarkerCluster(locations=locations, popups=popups, name="Cluster").add_to(m)
Explanation: The cells from [20] to [25] create a folium map with bokeh for the time-series at the observed points.
Note that we did mark the nearest model cell location used in the comparison.
End of explanation
titles = {
    "coawst_4_use_best": "COAWST_4",
    "global": "HYCOM",
    "NECOFS_GOM3_FORECAST": "NECOFS_GOM3",
    "NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST": "NECOFS_MassBay",
    "OBS_DATA": "Observations",
}
from itertools import cycle
from bokeh.embed import file_html
from bokeh.models import HoverTool
from bokeh.palettes import Category20
from bokeh.plotting import figure
from bokeh.resources import CDN
from folium import IFrame
# Plot defaults.
colors = Category20[20]
colorcycler = cycle(colors)
tools = "pan,box_zoom,reset"
width, height = 750, 250
def make_plot(df, station):
    p = figure(
        toolbar_location="above",
        x_axis_type="datetime",
        width=width,
        height=height,
        tools=tools,
        title=str(station),
    )
    for column, series in df.iteritems():
        series.dropna(inplace=True)
        if not series.empty:
            if "OBS_DATA" not in column:
                bias = mean_bias[str(station)][column]
                skill = skill_score[str(station)][column]
                line_color = next(colorcycler)
                kw = dict(alpha=0.65, line_color=line_color)
            else:
                skill = bias = "NA"
                kw = dict(alpha=1, color="crimson")
            legend = f"{titles.get(column, column)}"
            legend = legend.rstrip("fillmismatch").rstrip("#")
            line = p.line(
                x=series.index,
                y=series.values,
                legend=legend,
                line_width=5,
                line_cap="round",
                line_join="round",
                **kw,
            )
            p.add_tools(
                HoverTool(
                    tooltips=[
                        ("Name", "{}".format(titles.get(column, column))),
                        ("Bias", bias),
                        ("Skill", skill),
                    ],
                    renderers=[line],
                )
            )
    return p
def make_marker(p, station):
    lons = stations_keys(config, key="lon")
    lats = stations_keys(config, key="lat")
    lon, lat = lons[station], lats[station]
    html = file_html(p, CDN, station)
    iframe = IFrame(html, width=width + 40, height=height + 80)
    popup = folium.Popup(iframe, max_width=2650)
    icon = folium.Icon(color="green", icon="stats")
    marker = folium.Marker(location=[lat, lon], popup=popup, icon=icon)
    return marker
dfs = load_ncs(config)
for station in dfs:
    sta_name = all_obs[station]
    df = dfs[station]
    if df.empty:
        continue
    p = make_plot(df, station)
    marker = make_marker(p, station)
    marker.add_to(m)
folium.LayerControl().add_to(m)
m
Explanation: Here we use a dictionary with some models we expect to find so we can create a better legend for the plots. If any new models are found, we will use its filename in the legend as a default until we can go back and add a short name to our library.
End of explanation
<END_TASK>