写一个python程序：顺序读取文件夹下的.fits文件并重命名

好的，以下是一个简单的Python程序，它会顺序读取指定文件夹下的所有.fits文件，并将它们重命名为“file1.fits”、“file2.fits”、“file3.fits”等等。

import os

folder_path = "your/folder/path"
counter = 1

for filename in os.listdir(folder_path):
    if filename.endswith(".fits"):
        new_filename = "file" + str(counter) + ".fits"
        os.rename(os.path.join(folder_path, filename), os.path.join(folder_path, new_filename))
        counter += 1

请将 "your/folder/path" 替换为你想要读取文件的文件夹路径。这个程序会遍历整个文件夹，找到所有以“.fits”结尾的文件，并将它们重命名为“file1.fits”、“file2.fits”、“file3.fits”等等。

相关问题

写一个python程序：顺序读取文件夹下的.fits文件，将相邻的两个文件相减，并将相减结果保存到一个.txt文件

好的，可以使用以下代码实现您的需求：

import os
import numpy as np
from astropy.io import fits

# 定义.fits文件所在文件夹路径和.txt文件保存路径
fits_folder = '/path/to/fits/folder/'
txt_file = '/path/to/save/txt/file.txt'

# 获取.fits文件列表并排序
fits_files = os.listdir(fits_folder)
fits_files.sort()

# 初始化变量
prev_data = None
result_list = []

# 循环读取.fits文件并相减
for file in fits_files:
    if file.endswith('.fits'):
        # 读取.fits文件数据
        with fits.open(os.path.join(fits_folder, file)) as hdul:
            data = hdul[0].data.astype(np.float32)
        
        # 若不是第一个.fits文件，则与前一个文件相减
        if prev_data is not None:
            result = data - prev_data
            result_list.append(result)
        
        prev_data = data

# 将相减结果保存到.txt文件
with open(txt_file, 'w') as f:
    for result in result_list:
        for row in result:
            f.write('\t'.join(str(val) for val in row))
            f.write('\n')

需要注意的是，上述代码使用了Astropy库来读取.fits文件中的数据，如果您的系统中没有安装该库，可以通过以下命令进行安装：

pip install astropy

用python写一个用于.fits图像文件配准的生成对抗网络

配准图像是图像处理的一个重要问题，生成对抗网络（GAN）是近年来非常流行的图像生成模型，可以用于图像配准。下面是一个简单的用于.fits图像文件配准的GAN的Python实现：

首先，需要导入相关的Python库：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from astropy.io import fits
from tensorflow.keras import layers

然后，需要加载.fits文件，将其转换为numpy数组，并将其归一化到[-1, 1]之间：

def load_fits_file(file_path):
    hdul = fits.open(file_path)
    data = hdul[0].data.astype(np.float32)
    data /= np.max(np.abs(data))
    return data

接下来，需要定义生成器和判别器的架构：

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(7 * 7 * 256, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256)

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model


def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                            input_shape=[28, 28, 1]))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Flatten())
    model.add(layers.Dense(1))

    return model

然后，定义损失函数和优化器：

cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)


def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss


def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)


generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

接下来，需要定义训练循环：

def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, 100])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

最后，定义GAN模型并开始训练：

def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)

        if epoch % 100 == 0:
            generate_and_save_images(generator, epoch + 1, test_input)

        print('Epoch {} finished'.format(epoch + 1))

    generate_and_save_images(generator, epochs, test_input)


def generate_and_save_images(model, epoch, test_input):
    predictions = model(test_input, training=False)
    fig = plt.figure(figsize=(4, 4))

    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i + 1)
        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
        plt.axis('off')

    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()


BUFFER_SIZE = 60000
BATCH_SIZE = 256
EPOCHS = 300
train_images = load_fits_file('image.fits')
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
generator = make_generator_model()
discriminator = make_discriminator_model()
train(train_dataset, EPOCHS)

这个简单的GAN模型可以用于.fits图像文件配准，但是需要根据具体的需求进行修改和优化。