Homework Problem \#2 - Arctic Ice

• /1v1/08-LionGuo/02-Homework-Problem-2-Arctic-Iceproblem_2.ipynb

• /1v1/08-LionGuo/02-Homework-Problem-2-Arctic-Ice/day_2.ipynb

Hand-in format: IPython Notebook or python program. Submit via email.

As a reminder: please make sure your code is clean, documentated, and understandable. Make sure it runs without errors.

提交格式: IPython Notebook或python程序。通过电子邮件提交。

作为一个提醒:请确保您的代码是干净的、有文档记录的和可理解的。确保它没有错误地运行。

Background

The purpose of this problem is to become familiar with loading, manipulating, and analyzing image-like data, plotting it. We will use a dataset collected by the AMSR-E instrument Aqua satellite.

这个问题的目的是熟悉加载、操作和分析类似图像的数据，并绘制它。我们将使用AMSR-E仪器Aqua卫星收集的数据集。

The data consists of maps of the concentration of ice in the Arctic collected between 2006 and 2011. The data obtained from the amsr database and converted into a single HDF5 file format.

这些数据由2006年至2011年收集的北极冰浓度地图组成。从amsr数据库获得的数据，并转换为单一的HDF5文件格式。

Part 1 - Examining a single map

第1部分-检查单个地图

Begin by examining the HDF5 file - you can use h5ls at the command line, or h5py inside the notebook.

从检查HDF5文件开始-你可以在命令行中使用' h5ls '，或者在笔记本中使用' h5py '。

If you don't remember how to open HDF5 files, and read datasets from HDF5 files, look at our Day 2 lecture.

如果你不记得如何打开 HDF5 文件，并从 HDF5 文件中读取数据集，请参考我们第2天的课程。

There are many datasets, each with a name of the format YYYYMMDD, giving the data. Each dataset is a single map (i.e. 2D array), where the values give the ice concentration (fraction, from 0.0 to 100.0) in that pixel of the map. Careful of NaN values!

有许多数据集，每个数据集的名称格式为“YYYYMMDD”，用于提供数据。每个数据集都是一个单一的地图(即2D数组)，其中的值给出了地图像素中的冰浓度(从0.0到100.0的分数)。小心NaN值!

Read one of the maps, and plot it with Matplotlib.

阅读其中一张地图，并使用Matplotlib绘制它。

Note: to get the correct orientation, you need the origin='lower' argument for imshow(). Include a colorbar. Remove the tick labels (0, 100, and so on, indicating pixel number) since they are not useful.

注意:为了获得正确的方向，你需要' origin='lower' '参数为' imshow() '。包括一个colorbar。删除标记(“0”、“100”等，表示像素数)，因为它们没有用。

答案

# -*- coding: utf-8 -*-
# @Time    : 2022/10/6 18:26
# @Author  : AI悦创
# @FileName: hw2.py
# @Software: PyCharm
# @Blog    ：https://bornforthis.cn/
import h5py


# f = h5py.File('p2_icedata_area.hdf5', 'r')
# # f = h5py.File('p2_icedata.hdf5', 'r')
# print(f)
# print(f.keys())
# dataset = f["pixel_areas"]
# print(dataset)
# print(dataset.shape)
# print(dataset.dtype)

def read_hdf5(path):
    f = h5py.File(path, 'r')
    keys = f.keys()
    for key in keys:
        # print(key)
        dataset = f[key]
        print(key, dataset.shape, dataset.dtype, sep="\t")


if __name__ == '__main__':
    read_hdf5("p2_icedata.hdf5")

import matplotlib.pyplot as plt
import h5py
def read_hdf5(path):
    year_lst = []
    dataset_lst = []
    f = h5py.File(path, 'r')
    keys = f.keys()
    for key in keys:
        # print(key)
        dataset = f[key]
#         print(key, dataset.shape, dataset.dtype, sep="\t")
        year_lst.append(key)
        dataset_lst.append(dataset.shape)
    # "g" 表示红色，marksize用来设置'D'菱形的大小
    plt.plot(year_lst, dataset_lst, "g", marker='D', markersize=5, label="year")
    # 绘制坐标轴标签
    plt.xlabel("年")
    plt.ylabel("数据")
    plt.title("HDF5")
    # 显示图例
    plt.legend(loc="lower right")
    # 调用 text()在图像上绘制注释文本
    # x1、y1表示文本所处坐标位置，ha参数控制水平对齐方式, va控制垂直对齐方式，str(y1)表示要绘制的文本
#     for x1, y1 in zip(year_lst, dataset_lst):
#         plt.text(x1, y1, str(y1), ha='center', va='bottom', fontsize=10)
    # 保存图片
    plt.savefig("1.jpg")
    plt.show()


read_hdf5("p2_icedata.hdf5")

Part 2 - Ice concentration versus time

第二部分-冰浓度与时间的关系

We want to make a plot of the ice concentration over time.

我们要画出冰浓度随时间变化的曲线。

First, write a loop to read all the datasets of the HDF5 file (e.g. into a dict).

首先，编写一个循环来读取HDF5文件的所有数据集(例如到字典中)。

Then, write an analysis function frac_pixels_above(dict,value) which, for each array in the input dict, computes the fraction of pixels above the input value. Use this to make a plot of the number of pixels with concentration above 50%, versus time.

然后，编写一个分析函数' frac_pixels_above(dict,value) '，该函数对于输入dict中的每个数组，计算输入' value '以上像素的百分比。使用此方法绘制浓度超过50%的像素数量与时间的关系图。

Note: to include "time" on the x-axis of a plot, you may want to write a helper function to convert the dict keys from their YYYYMMDD string format into a 3-tuple of (year, month, day) integer values.

注意:要在图形的x轴上包含“时间”，您可能需要编写一个helper函数来将字典键从它们的“YYYYMMDD”字符串格式转换为3元组(年、月、日)整数值。

This can then be converted into fractional years (e.g. 1 July 2012 is 2012.5). For simplicity you can assume each month has 30 days.

这可以转换为小数年(例如，2012年7月1日为“2012.5”)。为了简单起见，你可以假设每个月有30天。

Try experimenting with matplotlib set_major_formatter to get a good representation of dates in the tick labels.

尝试使用matplotlib ' set_major_formatter '来获得一个很好的在标记标签中日期的表示。

Describe what you see in the plot.

描述你在情节中看到了什么。

def read_hdf5(path):
    # year_lst = []
    dataset_lst = []
    f = h5py.File(path, 'r')
    keys = f.keys()
    for key in keys:
        # print(key)
        dataset = f[key]
        # print(key, dataset.shape, dataset.dtype, sep="\t")
        # year_lst.append(key)
        dataset_lst.append((key, dataset.shape))
    return dict(dataset_lst)

Part 3 - Physical units

第3部分-物理单元

To be more quantitative we will compute the actual surface area of Earth in $\rm{km}^2$ over which the ice concentration is above a given threshold.

为了更定量，我们将以$\rm{km}^2$为单位计算地球的实际表面积，在此范围内冰浓度超过给定的阈值。

However, these maps are projections of a spherical surface, so pixels have different area.

然而，这些地图是球面的投影，所以像素有不同的面积。

Every map uses the same projection, so the pixel areas in each are the same.

每个地图使用相同的投影，所以每个地图中的像素区域是相同的。

The areas (in $\rm{km}^2$) are available in the file named data/p2_icedata_area.hdf5. Inspect, then load, this datafile. Plot it (with colorbar and units).

区域(在$\rm{km}^2$中)在名为“data/p2_icedata_area.hdf5”的文件中可用。检查并加载这个数据文件。绘制它(用颜色条和单位)。

• p2_icedata_area.hdf5
• p2_icedata.hdf5

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