s-parameter_utilites_101.xls

s-parameter_utilites_101.xls 是一个用于处理 S 参数的工具文件。S 参数是一种特定的参数类型，用于描述电子元器件在高频电路中的传输特性。

该工具文件包含了一些常见的 S 参数计算和转换函数，可以帮助工程师更方便地分析和设计高频电路。它提供了一种直观的操作界面，使用户可以轻松地输入和处理 S 参数数据。

该工具文件的主要功能包括：

1. 导入和导出 S 参数数据：用户可以将已有的 S 参数数据导入到工具文件中进行处理，也可以将处理好的数据导出保存。这样，工程师可以在不同的工作环境中共享和使用 S 参数数据。

2. S 参数计算：工具文件提供了常见的 S 参数计算功能，比如计算反射系数、传输系数和增益等。通过输入电子元器件的特定参数，工具文件可以自动计算出相应的 S 参数。

3. S 参数转换：有时候，工程师需要将 S 参数数据从一个参考平面转换到另一个参考平面，或者从一个坐标系转换到另一个坐标系。该工具文件提供了方便的 S 参数转换功能，可以帮助工程师进行这些转换。

4. 频率响应分析：工具文件可以根据输入的 S 参数数据，绘制出频率响应曲线。这样，工程师可以直观地了解电子元器件的高频特性，并作出相应的设计决策。

总之，s-parameter_utilites_101.xls 是一个方便实用的工具文件，可以帮助工程师更好地处理和分析 S 参数数据，以便在高频电路设计中取得更好的性能。

相关问题

python写一段目标检测代码

以下是一段使用 TensorFlow Object Detection API 进行目标检测的 Python 代码：

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# Load the label map.
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

    sess = tf.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread('image.jpg')
image_expanded = np.expand_dims(image, axis=0)

# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_expanded})

# Draw the results of the detection (aka 'visulaize the results')

vis_util.visualize_boxes_and_labels_on_image_array(
    image,
    np.squeeze(boxes),
    np.squeeze(classes).astype(np.int32),
    np.squeeze(scores),
    category_index,
    use_normalized_coordinates=True,
    line_thickness=8,
    min_score_thresh=0.60)

# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)

# Press any key to close the image
cv2.waitKey(0)

# Clean up
cv2.destroyAllWindows()