semvec/pypi-packages huggingface dataset:
es-simple 0.0.7
A package for query ElasticSearch and parse response easy
Requires Python: >=3.8

# es_simple

A package for query ElasticSearch and parse response easy.

## Usage

```python
import es_simple
import time
qb = es_simple.QueryBuilder("hostname:*live*", # query string
                            int(time.time() * 1000 - 3600 * 1000), "start timestamp in ms"
                            int(time.time() * 1000), # end timestamp in ms
                            "5m", # interval
                            "log_timestamp", # time field
                            "Asia/Shanghai")  # time zone
  

qb.add_metric("avg_tcp_re_rate", "avg", "tcp_re_rate")
qb.add_metric("avg_cpu", "avg", "cpu_idle")
qb.add_date_histogram_bucket("ts")
qb.add_terms_bucket("nodename", "nodename", "20", {"tcp_re_rerate": "desc"})

es_query = qb.build()

print(es_query)
```
tsp-ip 0.1.3
TSP-IP (Travelling Salesman Problem integer programing)
Requires Python: >=3.11

# tsp-ip
TSP-IP (Travelling Salesman Problem integer programing)

Installing the package

	pip install tsp-ip

Example 1

```python
import numpy as np
import networkx as nx
from tsp_ip import tsp_ip

n = 20
print('Graph size = ', n)
matrix = np.random.randint(1, 99, (n, n))
print(matrix)

# Find the solution init as matrix numpy
path_len, graph_result = tsp_ip(matrix)

if path_len:
    # Get the list of edges of the optimal path
    print('Min path =', nx.find_cycle(graph_result))
    print('Length =', path_len)
else:
    print('Solution impossible!')
```


Example 2

```python
from tsp_ip import tsp_ip
from math import hypot
import random as rnd
import networkx as nx
import matplotlib.pyplot as plt

def distance(point1, point2):
    return hypot(point2[0] - point1[0], point2[1] - point1[1])

n = 50
print('Graph size = ', n)
points = [(rnd.uniform(0, 1000), rnd.uniform(0, 1000)) for _ in range(n)]

init_graph = nx.DiGraph()
for i in range(n):
    for j in range(n):
        if j < i:
            length = round(distance(points[i], points[j]))
            init_graph.add_edge(i, j, weight=length)
            init_graph.add_edge(j, i, weight=length)

# Find the solution init as graph
path_len, graph_result = tsp_ip(init_graph, msg=True)

if path_len:
    # Get the list of edges of the optimal path
    print('Min path =', nx.find_cycle(graph_result))
    print('Length =', path_len)
else:
    print('Solution impossible!')

# Draw the graph
plt.figure(figsize=(10, 8))
plt.axis("equal")
nx.draw(graph_result, points, width=0, with_labels=True, node_size=0, font_size=9, font_color="blue", arrowsize=0.1)
nx.draw(graph_result, points, width=1, edge_color="red", style="-", with_labels=False, node_size=0, arrowsize=10)
plt.show()
```

For performance comparison, the implementation of the model in the MillerвЂ“TuckerвЂ“Zemlin formulation

```python
from tsp_ip import tsp_mtz
```

Feel the difference.
personage-net 1.0.6
一个在线工具
Requires Python: >=3.12

# 项目名称

工具大全

## 特性


- 支持前端项目的打包


## 开始

### 安装依赖

确保已安装以下环境：

- py 3.12.0 环境


安装项目依赖：

```bash
pip install -r requirements.txt