def _post_info(
self, shortcode_or_model: Union[str,
InstagramPost]) -> InstagramPost:
if isinstance(shortcode_or_model, InstagramPost):
return shortcode_or_model
shortcode = shortcode_or_model
d = Addict(self._web_api_client.media_info2(shortcode))
return InstagramPost(
post_num_id=d.id or None, # todo: this is actually a str
shortcode=d.shortcode or None,
img_height=_to_int(d.dimensions.height),
img_width=_to_int(d.dimensions.width),
display_url=d.display_url or None,
is_video=_to_bool(d.is_video),
caption_is_edited=_to_bool(d.caption_is_edited),
created_at=_timestamp_to_datetime(d.taken_at_timestamp),
like_count=_to_int(d.likes.count),
comment_count=_to_int(d.comments.count),
location_id=_to_int(d.location and d.location.id),
location_name=(d.location and d.location.name) or None,
location_address_json=(d.location and d.location.address_json)
or None,
owner_id=_to_int(d.owner.id),
owner_username=d.owner.username or None,
owner_full_name=d.owner.full_name or None,
is_ad=_to_bool(d.is_ad),
caption=d.caption.text or None,
users_in_photo=[p.user for p in d.users_in_photo],
hashtags=_get_hashtags(d.caption.text),
mentions=_get_mentions(d.caption.text),
)
def fit(self, df: pd.DataFrame) -> None:
self.columns = list(df.columns)
self.encoders = Addict()
for category_type, columns in self.columns_map.items():
for column_id, column in enumerate(columns):
encoder_class, parameters = self.type2encoder[category_type]
if category_type == "cyclical":
parameters["amplitude"] = self.columns_map[category_type][
column_id][1]
column = self.columns_map[category_type][column_id][0]
encoder = encoder_class(**parameters)
x = df[column].values
if category_type == "numerical":
x = x.reshape(-1, 1)
encoder.fit(x)
self.encoders[category_type][column] = encoder
self.column2type[column] = category_type
def main(config_file):
conf = Addict(yaml.safe_load(open(config_file, 'r')))
if conf.get("logging") is not None:
logging.config.dictConfig(conf["logging"])
else:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
raw_file = conf.get("output").get("existing_supermarkets_raw")
supermarkets = []
with open(raw_file, encoding='utf-8') as f:
for supermarket_raw in json.loads(f.read()):
supermarket_obj = {}
supermarket_obj["name"] = supermarket_raw.get("name")
supermarket_obj["addr"] = supermarket_raw.get("formatted_address")
geocode = supermarket_raw.get("geometry").get("location")
supermarket_obj["lat"] = geocode.get("lat")
supermarket_obj["lng"] = geocode.get("lng")
supermarket_obj["type"] = supermarket_raw.get("place_type")
supermarkets.append(supermarket_obj)
supermarkets_df = pd.DataFrame(supermarkets)
logging.info("%s supermarkets are located in the city",
supermarkets_df.shape[0])
supermarkets_df = supermarkets_df.drop_duplicates(subset=["lat", "lng"])
logging.info("There are %s supermarkets left after duplicates removed",
supermarkets_df.shape[0])
grocery_df = supermarkets_df.loc[supermarkets_df["type"] == "grocery"]
logging.info("%s of the results are grocery", grocery_df.shape[0])
supermarkets_df = supermarkets_df.reset_index()
supermarkets_df = supermarkets_df.loc[supermarkets_df["type"] ==
"supermarket"]
output_fp = conf.get("output").get("existing_supermarkets_data")
supermarkets_df.to_csv(output_fp, index=False)
logging.info("Information of existing supermarkets written to %s",
output_fp)
def immutable(cls, d):
'''If d is not an instance of Addict, turn it into Addict and lock it.
This is useful for configs and other static declarations.
'''
if not isinstance(d, Addict):
return LockedDict.lock(Addict(d))
return LockedDict.lock(d)
def all_pages(page_elem, iter_elem, func, *args, **kwargs):
page = 1
acc = []
while True:
paginated = Addict(func(*args, **kwargs, page=page))[page_elem]
acc.extend(paginated[iter_elem])
if int(paginated.page) >= int(paginated.pages):
return acc
page += 1
def _get_album_page(page, acc):
search_result = Addict(flickr.photosets.getList(page=page))
albums = search_result.photosets
for album in albums.photoset:
date_create = int(album.date_create)
dt_date_create = datetime.fromtimestamp(date_create, timezone.utc)
acc.append((album.title._content, dt_date_create, album.id))
if albums.page < albums.pages:
return _get_album_page(page + 1, acc)
else:
return acc
def main(config_file):
conf = Addict(yaml.safe_load(open(config_file, 'r')))
if conf.get("logging") is not None:
logging.config.dictConfig(conf["logging"])
else:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
supermarkets_file = conf.get("input").get("supermarkets_file")
logging.info("Loading geocode of supermarkets from %s", supermarkets_file)
supermarkets_file_reader = csv.reader(open(supermarkets_file))
supermarkets_file_header = next(supermarkets_file_reader)
supermarkets = []
for row in supermarkets_file_reader:
supermarket = dict(zip(supermarkets_file_header, row))
supermarkets.append(supermarket)
logging.info("%s supermarkets located in the city.", len(supermarkets))
grid_geocode_file = conf.get("input").get("grid_geocode_file")
logging.info("Loading geocode of city grids from %s", grid_geocode_file)
grids_file_reader = csv.reader(open(grid_geocode_file))
grids_file_header = next(grids_file_reader)
grids = []
for row in grids_file_reader:
grid = dict(zip(grids_file_header, row))
grids.append(grid)
logging.info("The city is covered by %s 1km x 1km grids.", len(grids))
api_key = conf.get("API").get("KEY")
gmaps = googlemaps.Client(key=api_key)
results = []
counter = 0
logging.info("Start querying driving time from city grid to supermarkets ...")
start_time = time.time()
for grid in grids:
for supermarket in supermarkets:
logging.debug("Processing grid: %s - supermarket: %s", grid, supermarket)
dist_api_worker = DistAPIWorker(gmaps, grid, supermarket)
response = dist_api_worker.run()
results.append(response)
counter += 1
if counter % 1000 == 0:
logging.info("%s grid-supermarket pair processed ... Elapsed time %s seconds",
counter, round(time.time() - start_time, 4))
# Export query responses to file
if len(results) > 0:
results_fp = conf.get("output").get("grid_to_supermarket_dist_raw")
with open(results_fp, 'w') as output_file:
json.dump(results, output_file, indent=4)
logging.info("%s query responses dumped to %s", len(results), results_fp)
def wrapper(*args: Any, **kwargs: Any) -> Any:
"""Wrap function for 'Dict'.
Args:
args: Arguments to pass into the wrapper.
kwargs: Arguments to pass into the wrapper.
Returns:
addict.Dict: Instance `Dict` object.
"""
lists = []
return_value = func(*args, **kwargs)
if isinstance(return_value, list):
for value in return_value:
if isinstance(value, dict):
lists.append(Addict(value))
else:
lists.append(value)
return lists
elif isinstance(return_value, dict):
return Addict(return_value)
else:
return return_value
def _paginate_thumb_feed(
self, feed_name: str, feed_kwargs: dict,
media_path: Iterator[str]) -> Iterator[InstagramPostThumb]:
has_next_page = True
end_cursor = None
while has_next_page:
r = getattr(self._web_api_client, feed_name)(**feed_kwargs,
end_cursor=end_cursor)
media = Addict(r)
for p in media_path:
media = media[p]
has_next_page = media.page_info.has_next_page
end_cursor = media.page_info.end_cursor
for edge in media.edges:
yield self._node_to_post_thumb(edge.node)
def _get_page_of_images_in_album(flickr, album_id, page, acc, output=False):
album_info = Addict(
flickr.photosets.getPhotos(
photoset_id=album_id,
page=page,
extras="url_m,date_taken,geo",
)).photoset
if output:
logger.info(
f"Processing album '{album_info.title}' with {album_info.total} "
"photos...")
acc.extend(album_info.photo)
# return album for data about it
return album_info
def _node_to_post_thumb(cls, data: dict) -> InstagramPostThumb:
data = Addict(data)
caption = _get_caption(data)
return InstagramPostThumb(
post_num_id=data.id,
owner_num_id=_to_int(data.owner.id),
caption=caption,
shortcode=data.shortcode or None,
comment_count=_to_int(data.edge_media_to_comment.count),
like_count=_to_int(data.edge_media_preview_like.count),
created_at=_timestamp_to_datetime(data.taken_at_timestamp),
img_height=_to_int(data.dimensions.height),
img_width=_to_int(data.dimensions.width),
img_url=data.display_url or None,
is_video=_to_bool(data.is_video),
hashtags=_get_hashtags(caption),
mentions=_get_mentions(caption),
)
def __init__(self, columns_map):
self.columns_map = columns_map
self.type2encoder = {
"numerical": (MinMaxScaler, {
"feature_range": (-1, 1)
}),
"categorical": (LabelEncoderUnseen, {}),
"cyclical": (CyclicEncoder, {}),
}
if "joined_encoders" in self.columns_map:
self.joined_encoders = self.columns_map["joined_encoders"]
del columns_map["joined_encoders"]
else:
self.joined_encoders = {}
if set(self.type2encoder.keys()).intersection(
columns_map.keys()) != set(columns_map.keys()):
raise ValueError(
f"Wrong column names in columns_map {columns_map}.")
self.category_types = set(self.columns_map.keys())
self.column2type = self.get_columns2type()
self.column_classes = Addict()
for category_type in self.category_types:
for column in columns_map[category_type]:
self.column_classes[column] = Column(
column, category_type=category_type)
for column_name, column_names in self.joined_encoders.items():
category_type = self.column2type[column_name]
for subcolumn_name in column_names:
self.column_classes[subcolumn_name] = Column(
subcolumn_name, category_type=category_type)
self.numerical_columns = self._get_columns("numerical")
self.categorical_columns = self._get_columns("categorical")
self.cyclical_columns = self._get_columns("cyclical")
def main(config_file):
conf = Addict(yaml.safe_load(open(config_file, 'r')))
if conf.get("logging") is not None:
logging.config.dictConfig(conf["logging"])
else:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
API_KEY = conf.get("API").get("KEY")
base_url = conf.get("API").get("URL")
# place_types = ["supermarkets", "convenience_store", "department_store", "store", "grocery"]
place_types = ["supermarkets", "grocery"]
locations = []
for place in place_types:
logging.info("Searching for %s in Penang", place)
query = "query=" + place + "+in+Penang"
url = base_url + query + "&key=" + API_KEY
logging.info("url of API query: %s", url)
response = requests.get(url)
results = json.loads(response.text).get("results")
logging.info("%s results are found.", len(results))
for result in results:
location = {}
location["name"] = result.get("name")
location["addr"] = result.get("formatted_address")
geocode = result.get("geometry").get("location")
location["lat"] = geocode.get("lat")
location["lng"] = geocode.get("lng")
location["type"] = place
locations.append(location)
places_df = pd.DataFrame(locations)
places_df = places_df.drop_duplicates(subset=["lat", "lng"])
places_df = places_df.reset_index()
output_fp = conf.get("output").get("filename")
places_df.to_csv(output_fp, index=False)
logging.info("%s supermarkets are located in the city", places_df.shape[0])
logging.info("Information of existing supermarkets written to %s",
output_fp)
def _user_info(self, u: Union[str, int]) -> InstagramUser:
"""
:param u: Prefer username (fewer gets)
:return:
"""
# todo: username <-> user_id bidict cache
if not isinstance(u, str) or u.isdigit():
# input is a user_id, and we have to
user_id = _to_int(u)
# todo: potential problem if user has no posts, how can we get username?
user_id = _to_int(u)
first_thumb = self.user_feed(user_id).limit(1).to_list()[0]
first_post = self._post_info(first_thumb.shortcode)
u = first_post.owner_username
username = u
d = Addict(self._web_api_client.user_info2(user_name=username))
return InstagramUser(
biography=d.biography or None,
website=d.website or None,
followed_by_count=_to_int(d.counts.followed_by),
follows_count=_to_int(d.counts.follows),
full_name=d.full_name or None,
user_id=_to_int(d.id),
is_business_account=_to_bool(d.is_business_account),
is_joined_recently=_to_bool(d.is_joined_recently),
is_private=_to_bool(d.is_private),
is_verified=_to_bool(d.is_verified),
profile_pic_url=d.profile_pic_url or None,
username=d.username or None,
connected_fb_page=d.connected_fb_page or None,
media_count=_to_int(d.counts.media),
)
flickr = auth_flickr()
# search_result = Addict(
# flickr.photos.search(
# user_id="me",
# tags="andonnee",
# tag_mode="all",
# min_uploaded_date="2021-10-25 00:00:00",
# per_page=500,
# extras="tags,machine_tags",
# )
# )
album = Addict(
flickr.photosets.getPhotos(
photoset_id="72157720209505213",
per_page=500,
))
start_id = "51730055105"
end_id = "51730863735"
is_process = False
# TODO replace wiht walk https://stuvel.eu/flickrapi-doc/7-util.html
for photo in album.photoset.photo:
if photo.id == end_id:
break
if photo.id == start_id:
is_process = True
from addict import Dict as Addict
from api_auth import auth_flickr
flickr = auth_flickr()
taken_date = "2020-11-14"
search_result = Addict(
flickr.photos.search(
user_id="me",
min_taken_date=f"{taken_date} 00:00:00",
max_taken_date=f"{taken_date} 23:59:59",
per_page=5,
))
print(f"Searching {taken_date}...")
# TODO replace wiht walk https://stuvel.eu/flickrapi-doc/7-util.html
for photo in search_result.photos.photo:
photo = Addict(flickr.photos.getInfo(photo_id=photo.id)).photo
title = photo.title._content
owner = photo.owner.nsid
id_ = photo.id
url = f"https://flickr.com/photos/{owner}/{id_}"
print(f"{url} {title}")
def gpx2flickr(
ctx,
gpx_filepath,
flickr_album,
delta,
delta_tz,
tolerance,
is_clear,
kml_output_path,
kml_thumbnail_size,
is_update_images,
api_key,
api_secret,
):
"""Add location information to Flickr images based on a GPX file"""
try:
logger.info("Parsing time shift...")
delta = process_delta(delta)
if delta_tz:
delta_tz = process_delta([delta_tz])
delta_total = delta + delta_tz
else:
delta_tz = None
delta_total = delta
# tz delta no different from delta for flickr (update time not supported)
# so do not print intermediate time deltas like for images
print_delta(delta, "Time")
tolerance = process_tolerance(tolerance)
gpx_segments = process_gpx(gpx_filepath)
token_cache_location = click.get_app_dir(DEFAULT_APP_DIR)
logger.info("Logging in to Flickr...")
flickr = create_flickr_api(api_key,
api_secret,
token_cache_location=token_cache_location)
user = Addict(flickr.urls.lookupUser(url=flickr_album.url)).user
logger.info("Synching Flickr Geo tags to GPX...")
if not is_update_images:
logger.warning(
"The images will not be updated with the positions!")
positions = synch_gps_flickr(
flickr,
user,
flickr_album,
gpx_segments,
delta_total,
tolerance,
is_clear,
is_update_images,
ctx.obj["DEBUG"],
)
def image_src(x):
return x.url_m
def image_name(x):
return create_photopage_url(x, user)
process_kml(positions, kml_output_path, kml_thumbnail_size, image_src,
image_name)
except Exception as ex:
logger.error("*** An unrecoverable error occured ***")
lf = logger.error if not ctx.obj["DEBUG"] else logger.exception
lf(str(ex))
sys.exit(1)
def main(config_file):
conf = Addict(yaml.safe_load(open(config_file, 'r')))
if conf.get("logging") is not None:
logging.config.dictConfig(conf["logging"])
else:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
start_time = time.time()
logging.info("Part I Load city grid layer")
grid_fp = conf.get("input").get("grid_file")
grid_df = pd.read_csv(grid_fp)
grid_df["id"] = grid_df["id"].apply(lambda grid_id: str(grid_id))
grid_df = grid_df.set_index("id")
grid_df = grid_df.dropna()
logging.info("Converting UTM coordinate system to geocode ...")
inProj = pyproj.Proj(init='epsg:3857')
outProj = pyproj.Proj(init='epsg:4326')
grid_df[["left_lng", "top_lat"]] = grid_df.apply(lambda row: convert_utm_coords(row[["left", "top"]], inProj, outProj), axis=1)
grid_df[["right_lng", "bottom_lat"]] = grid_df.apply(lambda row: convert_utm_coords(row[["right", "bottom"]], inProj, outProj), axis=1)
grid_df["center_lng"] = (grid_df["left_lng"] + grid_df["right_lng"]) / 2
grid_df["center_lat"] = (grid_df["top_lat"] + grid_df["bottom_lat"]) / 2
logging.info("Write grid center geocode to file")
grid_geocode_df = grid_df[["center_lng", "center_lat"]]
grid_geocode_file = conf.get("output").get("grid_geocode_file")
grid_geocode_df.to_csv(grid_geocode_file, index=True)
logging.info("Elapsed time %s seconds ...",
round(time.time() - start_time, 4))
logging.info("Part II Assign residential buildings to grids")
grid_dict = grid_df.to_dict("index")
buildings_fp = conf.get("input").get("residential_buildings_file")
buildings_df = pd.read_csv(buildings_fp)
logging.info("Range of longitude: %s - %s",
buildings_df["center_lng"].min(),
buildings_df["center_lng"].max())
logging.info("Range of latitude: %s - %s",
buildings_df["center_lat"].min(),
buildings_df["center_lat"].max())
buildings_df["grid"] = buildings_df.apply(lambda row: assign_grid(row[["center_lng", "center_lat"]], grid_dict), axis=1)
buildings_df = buildings_df.set_index("id")
logging.info("Elapsed time: %s seconds ...",
round(time.time() - start_time, 4))
logging.info("Part III Compute gridwise total floor area")
logging.info("Residential building types: %s",
buildings_df["type"].unique())
buildings_df[["area", "area_bungalow"]] = buildings_df.apply(lambda row: check_bungalow(row["type"], row["area"]), axis=1)
area_df = buildings_df.groupby(['grid'])['area', 'area_bungalow'].agg('sum')
area_df = pd.merge(area_df, grid_df, left_index=True, right_index=True)
area_df = area_df.drop(["left", "right", "top", "bottom",
"left_lng", "top_lat", "right_lng", "bottom_lat",
"center_lng", "center_lat"], axis=1)
area_df = area_df.reset_index()
logging.info("Shape of area_df: %s", area_df.shape)
logging.info(area_df.head())
logging.info("Elapsed time: %s seconds ...",
round(time.time() - start_time, 4))
logging.info("Part IV Distribute city population into grids")
district_df = area_df.groupby(["district"])['area', 'area_bungalow'].agg('sum')
district_df["total_population"] = conf.get("district_population")
district_df["bungalow_population"] = district_df["total_population"] / 100 * 5
district_df["apartment_population"] = district_df["total_population"] - district_df["bungalow_population"]
district_df = district_df.reset_index()
logging.info(district_df)
population_df = pd.merge(area_df, district_df[["district", "area", "apartment_population"]], on='district')
population_df = population_df.rename(columns={
"index": "grid_id",
"area_x": "area",
"area_bungalow_x": "area_bungalow",
"area_y": "area_apartment_district",
"apartment_population": "apartment_population_district"
})
population_df["population"] = population_df["apartment_population_district"] / population_df["area_apartment_district"] * population_df["area"] + \
population_df["area_bungalow"] / 100 * 5
population_df["grid_id"] = population_df["grid_id"].apply(lambda grid_id: int(grid_id))
logging.info("Shape of population_df: %s", population_df.shape)
logging.info(population_df.head())
logging.info("Part V Incorporate grid population with shape file")
grid_shape = conf.get("input").get("grid_shape_file")
sf = shp.Reader(grid_shape)
shp_df = read_shapefile(sf)
logging.info("Shape of shp_df: %s", shp_df.shape)
logging.info(shp_df.head())
population_shp_df = pd.merge(shp_df, population_df[["grid_id", "population"]],
left_on='id', right_on='grid_id', how='outer')
population_shp_df["population"].fillna(0, inplace=True)
population_shp_df = population_shp_df.drop(["grid_id", "coords"], axis=1)
logging.info("Export grid population to text file")
grid_population_file = conf.get("output").get("grid_population_file")
population_shp_df.to_csv(grid_population_file, index=False)
gdf = gpd.read_file(grid_shape)
gdf = gdf.to_crs({'init': 'epsg:3857'})
gdf["population"] = population_shp_df["population"]
grid_population_shape_file = conf.get("output").get("grid_population_shape_file")
gdf.to_file(grid_population_shape_file)
logging.info("Population info added to the shape file of city grid layer")
import json
from pathlib import Path
import webbrowser
from addict import Dict as Addict
import flickrapi
with open("api_key.json") as f:
flickr_key = Addict(json.load(f))
api_key = flickr_key.key
api_secret = flickr_key.secret
def auth_flickr():
flickr = flickrapi.FlickrAPI(
api_key,
api_secret,
format="parsed-json",
token_cache_location=Path("./.flickr").resolve(),
)
if not flickr.token_valid(perms="write"):
flickr.get_request_token(oauth_callback="oob")
authorize_url = flickr.auth_url(perms="write")
webbrowser.open_new_tab(authorize_url)
verifier = input("Verifier code: ")
flickr.get_access_token(verifier)
return flickr
from api_auth import auth_flickr
from flickr_utils import get_photos
def make_flickr_photo_url(photo, user_id):
if photo.pathalias:
user_path = photo.pathalias
else:
user_path = user_id
return f"https://www.flickr.com/photos/{user_path}/{photo.id}"
flickr = auth_flickr()
user = Addict(flickr.auth.oauth.checkToken())
album_id = "72157719125798869"
folder_path = (
"/Users/guilhem/Pictures/camera/___uploaded/20210508_foretlanfon_ok/autocorrect2"
)
file_pattern = "DSCF*.JPG"
photos = list(
get_photos(flickr, album_id, extras="date_taken,url_o,path_alias"))
flickr_time_index = {}
for photo in photos:
date_taken = photo.datetaken
flickr_time_index[date_taken] = photo
from addict import Dict as Addict
import dateutil
from dateutil.relativedelta import relativedelta
from api_auth import auth_flickr
flickr = auth_flickr()
search_result = Addict(
flickr.photos.search(
user_id="me",
tags="summer,ete,2020",
tag_mode="all",
min_taken_date="2019-01-16 00:00:00",
max_taken_date="2019-01-17 00:00:00",
extras="date_taken",
per_page=500,
))
for photo in search_result.photos.photo:
taken = photo.datetaken
print(f"Original: {taken}")
taken_dt = dateutil.parser.isoparse(taken)
delta = relativedelta(years=1, months=8, hours=-8)
taken_dt_corrected = taken_dt + delta
taken_corrected = taken_dt_corrected.strftime("%Y-%m-%d %H:%M:%S")
print(f"Corrected: {taken_corrected}")
resp = flickr.photos.setDates(photo_id=photo.id,
date_taken=taken_corrected)
def fit(self, df: pd.DataFrame) -> None:
for column in self.categorical_columns:
if df[column].dtype != "object":
raise TypeError(
f"We can process only string columns as categorical. "
f"We got {df[column].dtype} for {column}. "
f"Please cast 'str' on the column.")
self.columns = [x for x in df.columns if x in self.column2type]
self.encoders = Addict()
for column_name, subcolumn_names in self.joined_encoders.items():
if column_name in self.encoders:
raise ValueError(
f"We should not have same column in two joined columns! "
f"But we got it for {column_name}")
category_type = self.column2type[column_name]
encoder_class, parameters = self.type2encoder[category_type]
encoder = encoder_class(**parameters)
x = [df[column_name].values]
if category_type == "categorical":
for subcolumn_name in subcolumn_names:
x += [df[subcolumn_name].values]
x = np.concatenate(x)
encoder.fit(x)
self.encoders[column_name] = encoder
for subcolumn_name in subcolumn_names:
if subcolumn_name in self.encoders:
raise ValueError(
f"We should not have same subcolumn in two joined columns! "
f"But we got it for {subcolumn_name}")
self.encoders[subcolumn_name] = encoder
for category_type, column_names in self.columns_map.items():
encoder_class, parameters = self.type2encoder[category_type]
for column_name in column_names:
if column_name in self.encoders:
continue
if category_type == "cyclical":
parameters["amplitude"] = column_name[1]
column = column_name[0]
else:
column = column_name
encoder = encoder_class(**parameters)
x = df[column].values
encoder.fit(x)
self.encoders[column] = encoder
def main(config_file):
conf = Addict(yaml.safe_load(open(config_file, 'r')))
if conf.get("logging") is not None:
logging.config.dictConfig(conf["logging"])
else:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
raw_file = conf.get("output").get("grid_to_supermarket_dist_raw")
dist_data = []
with open(raw_file, encoding='utf-8') as f:
for dist_raw in json.loads(f.read()):
dist_obj = {}
dist_obj["grid_id"] = dist_raw["grid_id"]
dist_obj["supermarket_id"] = dist_raw["supermarket_id"]
dist_obj["status"] = dist_raw["status"]
if dist_raw.get("rows"):
row = dist_raw.get("rows")[0]
if row.get("elements"):
element = row.get("elements")[0]
if element.get("distance"):
dist_obj["distance"] = element.get("distance").get("value")
else:
dist_obj["distance"] = None
if element.get("duration"):
dist_obj["driving_time"] = element.get("duration").get("value")
else:
dist_obj["driving_time"] = None
else:
dist_obj["distance"] = None
dist_obj["driving_time"] = None
dist_data.append(dist_obj)
dist_df = pd.DataFrame(dist_data)
output_file = conf.get("output").get("grid_to_supermarket_dist_data")
dist_df.to_csv(output_file, index=False)
logging.info("%s distance query results written to %s",
len(dist_data), output_file)
supermarket_counts = {}
max_driving_time = int(conf.get("max_driving_time"))
for grid_id in dist_df["grid_id"].unique():
supermarket_counts[grid_id] = catch_supermarkets(grid_id, dist_df, max_driving_time)
population_file = conf.get("input").get("grid_population_file")
logging.info("Loading simulated population of city grids from %s", population_file)
population_df = pd.read_csv(population_file)
population_df["density"] = population_df.apply(lambda pop: \
compute_density(pop["id"], pop["population"], supermarket_counts), axis=1)
density_df = population_df[["id", "density"]]
grid_shape = conf.get("input").get("grid_shape_file")
sf = shp.Reader(grid_shape)
shp_df = read_shapefile(sf)
logging.info("Shape of shp_df: %s", shp_df.shape)
logging.info(shp_df.head())
density_shp_df = pd.merge(shp_df, density_df, left_on='id', right_on='id', how='outer')
density_shp_df = density_shp_df.drop("coords", axis=1)
logging.info("Export supermarket density to text file")
supermarket_density_file = conf.get("output").get("supermarket_density_file")
density_shp_df.to_csv(supermarket_density_file, index=False)
logging.info(density_shp_df.head())
gdf = gpd.read_file(grid_shape)
gdf = gdf.to_crs({'init': 'epsg:3857'})
gdf["density"] = density_shp_df["density"]
supermarket_density_shape_file = conf.get("output").get("supermarket_density_shape_file")
gdf.to_file(supermarket_density_shape_file)
logging.info("Supermarket density added to the shape file of city grid layer")