zephyr/zephyr-backend
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

noted orignal filename

Browse Source
This commit is contained in:
Dustella 2025-02-19 12:28:03 +08:00
parent 8fc63372f7
commit ed22e7d3a3
Signed by: Dustella
GPG Key ID: 35AA0AA3DC402D5C
9 changed files with 73 additions and 52 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
cosmic/multiple.py
View File

@ -1,3 +1,6 @@
# 这里全是重力波相关的
# 原始文件名 ：cosmic重力波多天.py
import os
import numpy as np
import pandas as pd
@ -269,7 +272,7 @@ def process_single_file(base_folder_path, i):
# 主循环，处理1到3个文件
base_folder_path = r"./cosmic/data/2008"
base_folder_path = r"./data/cosmic/2008"
all_mean_ktemp_Nz = []
all_mean_ktemp_Ptz = []
for file_index in range(1, 365):

13
cosmic/single.py
View File

@ -1,3 +1,5 @@
# 重力波 一天cosmic重力波
import os
import time
import numpy as np
@ -360,7 +362,8 @@ def plot_results_mean_ktemp_Nz(mean_ktemp_Nz, heights):
plt.ylabel('H(km)')
# plt.gca().invert_yaxis() # 使高度坐标轴从上到下递增，符合常规习惯
# plt.show()
def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights):
"""
@ -377,10 +380,10 @@ def plot_results_mean_ktemp_Ptz(mean_ktemp_Ptz, heights):
class SingleCosmicWavePlot:
def __init__(self, year, day):
BASE_PATH_COSMIC = DATA_BASEPATH.cosmic
base_folder_path = f"./{BASE_PATH_COSMIC}/{year}"
day_num = day
# 模块1调用
@ -407,9 +410,9 @@ class SingleCosmicWavePlot:
self.mean_ktemp_Ptz = mean_ktemp_Ptz
self.heights = heights
# 调用绘图模块函数进行绘图
def plot_results_mean_ktemp_Nz(self):
plot_results_mean_ktemp_Nz(self.mean_ktemp_Nz, self.heights)
def plot_results_mean_ktemp_Ptz(self):
plot_results_mean_ktemp_Ptz(self.mean_ktemp_Ptz, self.heights)

50
cosmic/temp_render.py
View File

@ -1,4 +1,5 @@
#此代码是对数据处理后的txt数据进行行星波参数提取绘图
# 原始文件 cosmic行星波参数全年逐日绘图
# 此代码是对数据处理后的txt数据进行行星波参数提取绘图
import pandas as pd
import numpy as np
@ -16,36 +17,35 @@ matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号
# 读取一年的数据文件
def temp_render(path:str = f"{DATA_BASEPATH.cosmic}/cosmic.txt", T = 16):
def temp_render(path: str = f"{DATA_BASEPATH.cosmic}/cosmic.txt", T=16):
def u_func(x, *params):
a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6, b6, a7, b7, a8, b8, a9, b9 = params
return (
a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1)
+ a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2)
+ a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3)
+ a4 * np.sin((2 * np.pi / T) * t - x + b4)
+ a5 * np.sin((2 * np.pi / T) * t + b5)
+ a6 * np.sin((2 * np.pi / T) * t + x + b6)
+ a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7)
+ a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8)
+ a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
a1 * np.sin((2 * np.pi / T) * t - 4 * x + b1)
+ a2 * np.sin((2 * np.pi / T) * t - 3 * x + b2)
+ a3 * np.sin((2 * np.pi / T) * t - 2 * x + b3)
+ a4 * np.sin((2 * np.pi / T) * t - x + b4)
+ a5 * np.sin((2 * np.pi / T) * t + b5)
+ a6 * np.sin((2 * np.pi / T) * t + x + b6)
+ a7 * np.sin((2 * np.pi / T) * t + 2 * x + b7)
+ a8 * np.sin((2 * np.pi / T) * t + 3 * x + b8)
+ a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
)
df = pd.read_csv(path, sep='\s+')
# 删除有 NaN 的行
df = df.dropna(subset=['Temperature'])
# 设置初始参数
# initial_guess = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] # v0, a1, b1, a2, b2, a3, b3
initial_guess = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5] # 9个 a 和 9个 b 参数
initial_guess = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # 9个 a 和 9个 b 参数
# 设置参数界限
bounds = (
[0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf], # 下界
[np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf,
np.inf, np.inf, np.inf]) # 上界
[0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf,
0, -np.inf, 0, -np.inf, 0, -np.inf, 0, -np.inf], # 下界
[np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf,
np.inf, np.inf, np.inf]) # 上界
# 用于存储拟合参数结果的列表
all_fit_results = []
@ -54,7 +54,7 @@ def temp_render(path:str = f"{DATA_BASEPATH.cosmic}/cosmic.txt", T = 16):
min_data_points = 36
# 进行多个时间窗口的拟合
#T应该取5、10、16
# T应该取5、10、16
if T not in [5, 10, 16]:
raise ValueError("T should be 5, 10, or 16")
for start_day in range(0, 365-3*T): # 最后一个窗口为[351, 366]
@ -77,17 +77,19 @@ def temp_render(path:str = f"{DATA_BASEPATH.cosmic}/cosmic.txt", T = 16):
temperature = np.array(df_8['Temperature']) # 温度，因变量
# 用T进行拟合
popt, pcov = curve_fit(u_func, x, temperature, p0=initial_guess, bounds=bounds, maxfev=50000)
popt, pcov = curve_fit(u_func, x, temperature,
p0=initial_guess, bounds=bounds, maxfev=50000)
# 将拟合结果添加到列表中
all_fit_results.append(popt)
# 将结果转换为DataFrame
columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4', 'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
columns = ['a1', 'b1', 'a2', 'b2', 'a3', 'b3', 'a4', 'b4',
'a5', 'b5', 'a6', 'b6', 'a7', 'b7', 'a8', 'b8', 'a9', 'b9']
fit_df = pd.DataFrame(all_fit_results, columns=columns) # fit_df即为拟合的参数汇总
# -------------------------------画图----------------------------
#a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅
# a1-a9,对应波数-4、-3、-2、-1、0、1、2、3、4的行星波振幅
a_columns = ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9']
k_values = list(range(-4, 5)) # 从 -4 到 4
@ -117,4 +119,4 @@ def temp_render(path:str = f"{DATA_BASEPATH.cosmic}/cosmic.txt", T = 16):
plt.xticks(ticks=tick_positions, labels=tick_labels)
plt.show() # 显示图形
plt.show() # 显示图形

1
saber/archive/gravity_plot.py
View File

@ -1,3 +1,4 @@
# saber 行星波参数一年逐日图
# 此代码是对数据处理后的txt数据进行行星波参数提取绘图
import pandas as pd

1
saber/archive/gravity_process.py
View File

@ -1,3 +1,4 @@
# 原始文件 saber行星波处理.py
# 高度、纬度可以指定，得到指定高度纬度下的数据，高度在101行，一般输入70、90、110，纬度在115行，纬度一般输入-20、30、60
import os

2
saber/archive/zjy_wave_fit_plot.py
View File

@ -1,3 +1,5 @@
# saber 重力波
from io import BytesIO
import netCDF4 as nc
import numpy as np

49
tidi/plot.py
View File

@ -1,3 +1,5 @@
# 原文件：TIDI循环重力波
import os
import pandas as pd
import numpy as np
@ -377,14 +379,13 @@ def process_vzonal_day(day, year=2015):
# 初始化一个空的DataFrame来存储所有天的结果
# -------------------------------------------------------------------------------------------
# --------meridional-------------------------------------------------------------------------
def process_vmeridional_day(day, year=2015):
try:
# 读取数据
base_path = DATA_BASEPATH.tidi
height_data = loadmat(rf"{base_path}/{year}/{day:03d}_Height.mat")
lat_data = loadmat(rf"{base_path}/{year}/{day:03d}_Lat.mat")
lon_data = loadmat(rf"{base_path}/{year}/{day:03d}_Lon.mat")
@ -757,16 +758,19 @@ def day_to_month(day):
if day <= cumulative_days:
return f'{["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"][i]}'
class TidiPlotv2:
def __init__(self, year):
self.year = year
cache_path = f"{DATA_BASEPATH.tidi}/cache"
if os.path.exists(f"{cache_path}/{year}/all_days_vzonal_results.parquet") \
and os.path.exists(f"{cache_path}/{year}/all_days_vmeridional_results.parquet"):
all_days_vzonal_results = pd.read_parquet(f"{cache_path}/{year}/all_days_vzonal_results.parquet")
all_days_vmeridional_results = pd.read_parquet(f"{cache_path}/{year}/all_days_vmeridional_results.parquet")
and os.path.exists(f"{cache_path}/{year}/all_days_vmeridional_results.parquet"):
all_days_vzonal_results = pd.read_parquet(
f"{cache_path}/{year}/all_days_vzonal_results.parquet")
all_days_vmeridional_results = pd.read_parquet(
f"{cache_path}/{year}/all_days_vmeridional_results.parquet")
else:
all_days_vzonal_results = pd.DataFrame()
# 循环处理每一天的数据
@ -775,7 +779,8 @@ class TidiPlotv2:
all_days_vzonal_results[rf"{day:02d}"] = u2
# 将结果按列拼接
all_days_vzonal_results.columns = [f"{day:02d}" for day in range(1, 365)]
all_days_vzonal_results.columns = [
f"{day:02d}" for day in range(1, 365)]
# 初始化一个空的DataFrame来存储所有天的结果
all_days_vmeridional_results = pd.DataFrame()
@ -785,33 +790,35 @@ class TidiPlotv2:
all_days_vmeridional_results[rf"{day:02d}"] = v2
# 将结果按列拼接
all_days_vmeridional_results.columns = [f"{day:02d}" for day in range(1, 365)]
all_days_vmeridional_results.columns = [
f"{day:02d}" for day in range(1, 365)]
# cache the results
# if dir not exists, create it
if not os.path.exists(f"{cache_path}/{year}"):
os.makedirs(f"{cache_path}/{year}")
all_days_vzonal_results.to_parquet(f"{cache_path}/{year}/all_days_vzonal_results.parquet")
all_days_vmeridional_results.to_parquet(f"{cache_path}/{year}/all_days_vmeridional_results.parquet")
all_days_vzonal_results.to_parquet(
f"{cache_path}/{year}/all_days_vzonal_results.parquet")
all_days_vmeridional_results.to_parquet(
f"{cache_path}/{year}/all_days_vmeridional_results.parquet")
self.all_days_vzonal_results = all_days_vzonal_results
self.all_days_vmeridional_results = all_days_vmeridional_results
# ---------------------------------------------------------------------------------------------------
# --------经纬向风平方和计算动能--------------------------------------------------------------------------------
# 使用numpy.where来检查两个表格中的对应元素是否都不是NaN
sum_df = np.where(
pd.notna(all_days_vmeridional_results) & pd.notna(all_days_vzonal_results),
pd.notna(all_days_vmeridional_results) & pd.notna(
all_days_vzonal_results),
all_days_vmeridional_results + all_days_vzonal_results,
np.nan
)
HP = 1/2*all_days_vmeridional_results+1/2*all_days_vzonal_results
heights = [70.0, 72.5, 75.0, 77.5, 80.0, 82.5, 85.0, 87.5, 90.0, 92.5,
95.0, 97.5, 100.0, 102.5, 105.0, 107.5, 110.0, 112.5, 115.0, 117.5, 120.0]
95.0, 97.5, 100.0, 102.5, 105.0, 107.5, 110.0, 112.5, 115.0, 117.5, 120.0]
HP.index = heights
# # 将 DataFrame 保存为 Excel 文件
# HP.to_excel('HP_data.xlsx')
@ -830,7 +837,7 @@ class TidiPlotv2:
data0_reversed[data0_reversed > 20] = float('nan')
# 转换成月份，365天
self.data0_reversed = data0_reversed
self.HP = HP
self.dates = dates
self.months = [day_to_month(day) for day in dates]
@ -859,7 +866,7 @@ class TidiPlotv2:
# 横坐标过长，设置等间隔展示
interval = 34 # 横坐标显示间隔
plt.xticks(ticks=range(0, len(dates), interval),
labels=months[::interval], rotation=45) # rotation旋转可不加
labels=months[::interval], rotation=45) # rotation旋转可不加
# 添加轴标签
plt.xlabel('Month') # X轴标签
@ -867,7 +874,7 @@ class TidiPlotv2:
# 显示图形
# plt.show()
def plot_month(self):
HP = self.HP
# --------------绘制月统计图-------------------------------------------------------------------
@ -905,7 +912,7 @@ class TidiPlotv2:
# 生成x轴的月份标签
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
# clear the last plot
plt.clf()

2
tidi/staged/plot.py
View File

@ -1,4 +1,4 @@
# 行星波
# 原始文件 TIDI行星波一年逐日图
# 此代码是对数据处理后的txt数据进行行星波参数提取绘图
# 2006_TIDI_V_Meridional_data.txt也可以做同样处理，一个是经向风提取的行星波，一个是纬向风的

2
tidi/staged/process.py
View File

@ -1,3 +1,5 @@
# 原始文件 TIDI行星波数据处理.py
import os
import numpy as np
from scipy.io import loadmat