text stringlengths 1 93.6k |
|---|
success += 1
|
log.info(str(_one)+' end')
|
except Exception:
|
log.exception('Failed in unknow err.')
|
log.info('error end')
|
finally:
|
log.info(f"共{_len_}个，成功安装了{success}个。")
|
pause()
|
# <FILESEP>
|
"""
|
This file can be used during development to automatically generate mock data from the API calls.
|
Please check CONTRIBUTING.md for details
|
"""
|
import hashlib
|
import json
|
import logging
|
import os
|
from pathlib import Path
|
import wrapt
|
from dynatrace import Dynatrace
|
from dynatrace.utils import slugify
|
@wrapt.patch_function_wrapper("dynatrace.http_client", "HttpClient.make_request")
|
def dump_to_json(wrapped, instance, args, kwargs):
|
r = wrapped(*args, **kwargs)
|
method = kwargs.get("method", "GET")
|
params = kwargs.get("params", "")
|
query_params = kwargs.get("query_params", "")
|
params = f"{params}" if params else ""
|
if query_params:
|
params += f"{query_params}"
|
if params:
|
encoded = f"{params}".encode()
|
params = f"_{hashlib.sha256(encoded).hexdigest()}"[:16]
|
path = slugify(args[0])
|
file_name = f"{method}{path}{params}.json"
|
file_path = f"test/mock_data/{file_name}"
|
if not Path(file_path).exists():
|
with open(file_path, "w") as f:
|
if r.text:
|
print(f"Dumping response to '{file_name}'")
|
json.dump(r.json(), f)
|
return r
|
def setup_log():
|
log = logging.getLogger(__name__)
|
log.setLevel(logging.DEBUG)
|
st = logging.StreamHandler()
|
fmt = logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(thread)d - %(filename)s:%(lineno)d - %(message)s")
|
st.setFormatter(fmt)
|
log.addHandler(st)
|
return log
|
def main():
|
dt = Dynatrace(os.getenv("DYNATRACE_TENANT_URL"), os.getenv("DYNATRACE_API_TOKEN"), log=setup_log())
|
# TODO - Code here as you add new endpoints, during development
|
# Any requests are going to be recorded in the `test/mock` folder and can later be used to write tests.
|
for m in dt.metrics.list(page_size=500):
|
print(m.metric_id)
|
if __name__ == "__main__":
|
main()
|
# <FILESEP>
|
from __future__ import print_function, division
|
import torch
|
import numpy as np
|
import BPnP
|
import matplotlib.pyplot as plt
|
import kornia as kn
|
from scipy.io import savemat, loadmat
|
device = 'cuda'
|
cube = loadmat('demo_data/cube.mat')
|
pts3d_gt = torch.tensor(cube['pts3d'], device=device, dtype=torch.float)
|
n = pts3d_gt.size(0)
|
poses = loadmat('demo_data/poses.mat')
|
P = torch.tensor(poses['poses'][0],device=device).view(1,6) # camera poses in angle-axis
|
q_gt = kn.angle_axis_to_quaternion(P[0,0:3])
|
fx = 800
|
fy = 700
|
u = 400
|
v = 300
|
K = torch.tensor(
|
[[fx, 0, u],
|
[0, fy, v],
|
[0, 0, 1]],
|
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