def test_test(model, test_data, test_examples=5):
# Meta-test-test
# given a meta-test-trained model, evaluate accuracy on the held out set
# of classes used
x, y = test_data
with torch.no_grad():
logits = model(x)
# report performance per class
ys = list(divide_chunks(y, test_examples))
tasks = list(divide_chunks(logits, test_examples))
t_accs = [
torch.eq(task.argmax(dim=1), ys).sum().item() / test_examples
for task, ys in zip(tasks, ys)
]
return t_accs
def retrieve_from_nwis(site_codes, start_date, end_date, n_per_chunk=1):
chunked_list = divide_chunks(site_codes, n_per_chunk)
df_list = []
for site_code_chunk in chunked_list:
d = st.get_streamflow_data(site_code_chunk, start_date, end_date, 'iv',
'15T')
df_list.append(d)
df_comb = pd.concat(df_list, 1)
return df_comb
def make_blank_weight_grid(catchment_ids, grid_ids, out_zarr):
catchment_chunk_size = 10000
chunked_catchments = divide_chunks(catchment_ids, catchment_chunk_size)
i = 0
for indices in chunked_catchments:
print(f'doing chunk {i}', flush=True)
blank = pd.DataFrame(0, index=indices, columns=grid_ids, dtype='float32')
col_name = 'nldas_grid_no'
idx_name = 'nhd_comid'
chunks = {col_name: 10000, idx_name: 30000}
ds = convert_df_to_dataset(blank, col_name, idx_name, 'weight',
chunks)
ds.to_zarr(out_zarr, mode='a', append_dim=idx_name)
def read_signals(self, max_size):
data_array = []
while len(data_array) < max_size: # 14 x 640 = 8960
second_array = []
while len(second_array) < 1792:
data = self.socket.recv(
self.buffer_size).decode('utf-8').split("\r\n")
for data_chunk in data:
converted_data = []
for elem in data_chunk.split(","):
try:
converted_data.append(int(elem))
except:
pass
second_array.extend(converted_data)
time.sleep(0.5)
data_array.extend(second_array)
return list(divide_chunks(data_array[:max_size], 14))
def sample_test(self, num_tasks, train_examples=15, device="cuda"):
assert num_tasks < len(
self.tasks_test
), f"Number of tasks requested is too large: {num_tasks} > {len(self.tasks_test)}"
assert (
train_examples <= 20
), f"Number of examples requested is too large: {train_examples} > 20"
# chose the n tasks to use
tasks = choice(self.tasks_test, size=num_tasks, replace=False)
# get the 20 indexes of each task (they are sequential)
task_ids = [range(task * 20, (task + 1) * 20) for task in tasks]
# split each group of 20 ids into (usually) 15 train and 5 test, unzip to separate train and test sequences
train_tasks, test_tasks = unzip(
train_test_split(ids, train_size=train_examples, shuffle=True)
for ids in task_ids)
# assemble the train/test trajectories
train_traj = [
self.cifar_test[i] for train_task in train_tasks
for i in train_task
]
test_traj = [
self.cifar_test[i] for test_task in test_tasks for i in test_task
]
# test-train examples are divided by task and sent to device (cpu/cuda)
chunk2device = lambda chunk: [(im.to(device), label.to(device))
for im, label in chunk]
train_tasks = [
chunk2device(chunk)
for chunk in divide_chunks(train_traj, n=train_examples)
]
# test-test tasks are collected into a massive tensor for one-pass evaluation
ims, labels = list(zip(*test_traj))
test_data = (torch.cat(ims).to(device), torch.cat(labels).to(device))
return train_tasks, test_data, tasks
def record_data(self, task, preprocess=True):
samples_to_collect = task.get_run_time() * self.sample_rate
channels = 14
samples_per_chunk = 80
chunks = int(samples_to_collect / samples_per_chunk)
data_array = np.zeros((channels, chunks, samples_per_chunk))
data = self.signal_reader.read_signals(8960)
# print(len(data))
# (640, 14) => (14, 640)
data = np.array(data).swapaxes(0, 1)
if preprocess:
for i, channel_data in enumerate(data):
processed_data = preprocess_data(channel_data,
sample_rate=128,
notch=True,
bp_filter=True,
artifact_removal=True)
data_array[i] = list(divide_chunks(processed_data, 80))
else:
data_array = data
# (14, 8, 80) => (14, 80, 8) => (8, 80, 14)
samples = data_array.swapaxes(1, 2).swapaxes(0, 2)
labels = [task.get_task_type()] * 8 # all 8 labels have same target
# save all data for transfer learning
if self.transfer_learning:
self.recorded_data['samples'].append(samples)
self.recorded_data['labels'].extend(labels)
task_data = {"samples": samples, "labels": labels}
return task_data
def get_all_streamflow_data(output_file,
sites_file,
huc2=None,
num_sites_per_chunk=5,
start_date="1970-01-01",
end_date='2019-01-01',
time_scale='H',
output_format='zarr',
num_site_chunks_write=6,
s3=False):
"""
gets all streamflow data for a date range for a given huc2. Calls are
chunked by station
:param output_file: [str] path to the csv file or zarr store where the data
will be stored
:param sites_file: [str] path to file that contains the nwis site
information
:param huc2: [str] zero-padded huc 2 (e.g., "02")
:param num_sites_per_chunk: [int] the number of sites that will be pulled
at in each web service call
:param start_date: [str] the start date of when you want the data for
(e.g., "1980-01-01")
:param end_date: [str] the end date of when you want the data for
(e.g., "1990-01-01")
:param time_scale: [str] Pandas like time string for the time scale at which
the data will be aggregated (e.g., 'H' for hour or 'D' for daily)
:param output_format: [str] the format of the output file. 'csv' or 'zarr'
:param num_site_chunks_write:
:param S3:
:return: None
"""
product = get_product_from_time_scale(time_scale)
site_codes = get_site_codes(sites_file, huc2)
not_done_sites = get_indices_not_done(output_file,
site_codes,
'site_code',
output_format,
is_column=False,
s3=s3)
site_codes_chunked = divide_chunks(not_done_sites, num_sites_per_chunk)
# loop through site_code_chunks
chunk_dfs = []
i = 0
for site_chunk in site_codes_chunked:
last_chunk = False
if site_chunk[-1] == not_done_sites[-1]:
last_chunk = True
streamflow_df_sites = None
# catch if there is a problem on the server retrieving the data
try:
streamflow_df_sites = get_streamflow_data(site_chunk, start_date,
end_date, product,
time_scale)
except json.decoder.JSONDecodeError:
continue
if streamflow_df_sites is not None:
chunk_dfs.append(streamflow_df_sites)
# add the number of stations for which we got data
i += streamflow_df_sites.shape[1]
if not i % (num_site_chunks_write * num_sites_per_chunk) or \
last_chunk:
print('writing out', flush=True)
write_out_chunks(chunk_dfs, output_file, output_format)
chunk_dfs = []
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(*args, **kwargs)
context['marketing_list_chunks'] = divide_chunks(
self.get_queryset().filter(marketing_subscription=True), 90)
return context
def echo(update, context):
if update.message.text == '📆 Отримати миттєві новини за добу':
digest = get_immediately_digest()
for item in digest:
context.bot.send_message(chat_id=update.message.chat_id,
text="{}\n{}".format(
item['title'], item['link']))
if update.message.text == '◀ Назад':
custom_keyboard = divide_chunks(main_menu, 2)
reply_markup = telegram.ReplyKeyboardMarkup(main_menu)
context.bot.send_message(chat_id=update.message.chat_id,
text="Головне меню",
reply_markup=reply_markup)
if update.message.text == '📌 Налаштувати категорії':
custom_keyboard = divide_chunks(tags, 2)
reply_markup = telegram.ReplyKeyboardMarkup(custom_keyboard)
context.bot.send_message(chat_id=update.message.chat_id,
text="Обирайте категорії новин",
reply_markup=reply_markup)
if update.message.text == '🕓 Налаштувати час':
if len(context.user_data['categories']) > 0:
custom_keyboard = divide_chunks(times, 2)
reply_markup = telegram.ReplyKeyboardMarkup(custom_keyboard)
context.bot.send_message(chat_id=update.message.chat_id,
text="Оберіть час отримання дайджесту",
reply_markup=reply_markup)
else:
context.bot.send_message(chat_id=update.message.chat_id,
text="⚠\nСпершу оберіть категорії")
if update.message.text == '🔧 Мої налаштування':
if context.user_data and len(
context.user_data['categories']) > 0 and context.user_data.get(
'time') is not None:
context.bot.send_message(
chat_id=update.message.chat_id,
text=
"<b>Ви підписані на категорії:</b>\n✅ {}. \n⌚ Час отримання дайджесту: {}. \n• Змінити налаштування /reset\n"
.format(',\n✅ '.join(context.user_data['categories']),
context.user_data['time']),
parse_mode=telegram.ParseMode.HTML)
elif load_from_db_by_chat_id(chat_id=update.message.chat_id):
user_data = {}
user_data['categories'] = load_from_db_by_chat_id(
chat_id=update.message.chat_id)[0][2].strip('{ }').replace(
'"', '').replace(' ', '').split(',')
user_data['time'] = load_from_db_by_chat_id(
chat_id=update.message.chat_id)[0][3]
context.bot.send_message(
chat_id=update.message.chat_id,
text=
"<b>Ви підписані на категорії:</b>\n✅ {}. \n⌚ Час отримання дайджесту: {}. \n• Змінити налаштування /reset\n"
.format(',\n✅ '.join(user_data['categories']),
user_data['time']),
parse_mode=telegram.ParseMode.HTML)
else:
context.bot.send_message(chat_id=update.message.chat_id,
text="⚠\nВи ще не налаштували дайджест")
if update.message.text in tags and update.message.text != '◀ Назад':
categories_handler(update, context)
if update.message.text in times and update.message.text != '◀ Назад':
time_handler(update, context)
