357 lines
14 KiB
Python
357 lines
14 KiB
Python
# 探空气球只有重力波
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from io import BytesIO
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from windrose import WindroseAxes
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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def plot_w_f(filtered_df, ax):
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min_val = 1
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max_val = 10
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bin_width = 0.5
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["w_f"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("w/f值统计结果", fontsize=24)
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ax.set_xlabel("w/f")
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ax.set_ylabel("Occurrence(%)")
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def plot_zhou_qi(filtered_df, ax):
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min_val = np.floor(filtered_df["zhou_qi"].min())
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max_val = np.ceil(filtered_df["zhou_qi"].max())
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bin_width = 1
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["zhou_qi"], bins=bins,
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kde=False, edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("周期统计结果", fontsize=24)
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ax.set_xlabel("h")
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ax.set_ylabel("Occurrence(%)")
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def plot_ver_wave_len(filtered_df, ax):
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min_val = np.floor(filtered_df["ver_wave_len"].min())
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max_val = np.ceil(filtered_df["ver_wave_len"].max())
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bin_width = 0.5
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["ver_wave_len"], bins=bins,
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kde=False, edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("垂直波长分布", fontsize=24)
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ax.set_xlabel("Vertical wavelength(km)")
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ax.set_ylabel("Occurrence(%)")
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def plot_hori_wave_len(filtered_df, ax):
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min_val = np.floor(filtered_df["hori_wave_len"].min())
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max_val = np.ceil(filtered_df["hori_wave_len"].max())
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bin_width = 100
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["hori_wave_len"], bins=bins,
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kde=False, edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("水平波长分布", fontsize=24)
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ax.set_xlabel("Horizontal wavelength(km)")
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ax.set_ylabel("Occurrence(%)")
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def plot_c_x(filtered_df, ax):
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min_val = np.floor(filtered_df["c_x"].min())
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max_val = np.ceil(filtered_df["c_x"].max())
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bin_width = 10
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["c_x"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("纬向本征相速度", fontsize=24)
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ax.set_xlabel("Zonal phase speed (m/s)")
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ax.set_ylabel("Occurrence (%)")
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def plot_c_y(filtered_df, ax):
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min_val = np.floor(filtered_df["c_y"].min())
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max_val = np.ceil(filtered_df["c_y"].max())
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bin_width = 10
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["c_y"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("经向本征相速度", fontsize=24)
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ax.set_xlabel("Meridional phase speed (m/s)")
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ax.set_ylabel("Occurrence (%)")
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def plot_c_z(filtered_df, ax):
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min_val = filtered_df["c_z"].min()
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max_val = filtered_df["c_z"].max()
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bin_width = 0.1
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["c_z"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("垂直本征相速度", fontsize=24)
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ax.set_xlabel("Vertical phase speed (m/s)")
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ax.set_ylabel("Occurrence (%)")
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def plot_u1(filtered_df, ax):
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min_val = np.floor(filtered_df["u1"].min())
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max_val = np.ceil(filtered_df["u1"].max())
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bin_width = 0.5
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["u1"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title(" ", fontsize=24)
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ax.set_xlabel("Zonal wind amplitude (m/s)")
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ax.set_ylabel("Occurrence (%)")
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def plot_v1(filtered_df, ax):
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min_val = np.floor(filtered_df["v1"].min())
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max_val = np.ceil(filtered_df["v1"].max())
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bin_width = 0.5
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["v1"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("扰动振幅统计结果", fontsize=24)
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ax.set_xlabel("Meridional wind amplitude (m/s)")
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ax.set_ylabel("Occurrence (%)")
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def plot_T1(filtered_df, ax):
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min_val = np.floor(filtered_df["T1"].min())
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max_val = np.ceil(filtered_df["T1"].max())
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bin_width = 0.5
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df["T1"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title(" ", fontsize=24)
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ax.set_xlabel("Temperature amplitude (K)")
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ax.set_ylabel("Occurrence (%)")
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def plot_MFu(filtered_df1, ax):
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min_val = np.floor(filtered_df1["MFu"].min())
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max_val = np.ceil(filtered_df1["MFu"].max())
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bin_width = 0.1
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df1["MFu"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("纬向动量通量统计结果", fontsize=24)
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ax.set_xlabel("Zonal momentum flux(mPa)")
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ax.set_ylabel("Occurrence(%)")
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def plot_MFv(filtered_df1, ax):
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min_val = np.floor(filtered_df1["MFv"].min())
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max_val = np.ceil(filtered_df1["MFv"].max())
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bin_width = 0.1
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bins = np.arange(min_val, max_val + bin_width, bin_width)
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sns.histplot(filtered_df1["MFv"], bins=bins, kde=False,
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edgecolor="black", stat="percent", ax=ax)
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ax.set_xlim(min_val, max_val)
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ax.set_title("经向动量通量统计结果", fontsize=24)
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ax.set_xlabel("Meridional momentum flux(mPa)")
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ax.set_ylabel("Occurrence(%)")
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def plot_horizontal_propagation(filtered_df: pd.DataFrame, season):
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def get_season(date):
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month = date.month
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if month in [12, 1, 2]:
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return "Winter"
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elif month in [3, 4, 5]:
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return "Spring"
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elif month in [6, 7, 8]:
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return "Summer"
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else:
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return "Fall"
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fig = plt.figure(figsize=(36, 20))
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gs = gridspec.GridSpec(2, 2)
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ax10 = []
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for i in range(4):
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ax10.append(fig.add_subplot(gs[i//2, i % 2], projection="windrose"))
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data = filtered_df.copy()
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data.loc[:, "date"] = data["file_name"].str[-28:-16]
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filtered_df = data[[
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"date"] + [col for col in data.columns if col != "file_name" and col != "date"]]
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filtered_df.reset_index(drop=True, inplace=True)
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filtered_df = filtered_df.drop_duplicates(
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subset="date", keep="last") # 使用 drop_duplicates 函数，保留每组重复中的最后一个记录
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filtered_df["date1"] = filtered_df["date"].str.split(
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"T").str[0] # 再加一列，只保留日期部分
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filtered_df["date1"] = pd.to_datetime(
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filtered_df["date1"], format="%Y%m%d") # 确保 'date1' 列是日期格式
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filtered_df["season"] = filtered_df["date1"].apply(get_season) # 添加季节列
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seasons = ["Winter", "Spring", "Summer", "Fall"]
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for ax, season in zip(ax10, seasons):
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season_data = filtered_df[filtered_df["season"] == season]
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windrose = WindroseAxes.from_ax(ax)
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ax.set_title(season, fontsize=18)
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windrose.bar(season_data["b"], np.ones_like(
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season_data["b"]), normed=False) # normed=True表示占每个季节的占比
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# season_data = filtered_df[filtered_df["season"] == season]
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# windrose = WindroseAxes.from_ax(ax)
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# ax.set_title(season, fontsize=18)
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# windrose.bar(season_data["b"], np.ones_like(
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# season_data["b"]), normed=False)
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def plot_vertical_propagation(filtered_df, ax):
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data = filtered_df.copy()
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data.loc[:, "date"] = data["file_name"].str[-28:-16]
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filtered_df = data[[
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"date"] + [col for col in data.columns if col != "file_name" and col != "date"]]
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filtered_df.reset_index(drop=True, inplace=True)
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filtered_df = filtered_df.drop_duplicates(
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subset="date", keep="last") # 使用 drop_duplicates 函数，保留每组重复中的最后一个记录
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filtered_df["date1"] = filtered_df["date"].str.split(
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"T").str[0] # 再加一列，只保留日期部分
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filtered_df["date1"] = pd.to_datetime(
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filtered_df["date1"], format="%Y%m%d") # 确保 'date1' 列是日期格式
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filtered_df.set_index("date1", inplace=True)
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monthly_stats_df = (
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filtered_df.groupby([filtered_df.index.month, filtered_df.index.year])
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.apply(lambda x: pd.Series({"Upload (%)": (x["a"] == 1).mean() * 100, "Downward (%)": (x["a"] == -1).mean() * 100}))
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.reset_index(level=1, drop=True)
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)
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monthly_avg_stats_df = monthly_stats_df.groupby(level=0).mean()
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dates = monthly_avg_stats_df.index.to_numpy()
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ax.plot(dates, monthly_avg_stats_df["Upload (%)"].to_numpy(
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), marker="o", label="Up (%)")
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ax.plot(dates, monthly_avg_stats_df["Downward (%)"].to_numpy(
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), marker="o", label="Down (%)")
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ax.set_xticks(
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ticks=np.arange(1, 13), labels=["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], rotation=0
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)
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ax.legend()
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ax.set_title("每月上传/下传重力波占比", fontsize=24)
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ax.set_xlabel("Month")
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ax.set_ylabel("Percentage (%)")
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def plot_energy_distribution(filtered_df, ax):
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data = filtered_df.copy()
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data.loc[:, "date"] = data["file_name"].str[-28:-16]
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filtered_df = data[[
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"date"] + [col for col in data.columns if col != "file_name" and col != "date"]]
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filtered_df.reset_index(drop=True, inplace=True)
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filtered_df = filtered_df.drop_duplicates(
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subset="date", keep="last") # 使用 drop_duplicates 函数，保留每组重复中的最后一个记录
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filtered_df["date1"] = filtered_df["date"].str.split(
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"T").str[0] # 再加一列，只保留日期部分
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filtered_df["date1"] = pd.to_datetime(
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filtered_df["date1"], format="%Y%m%d") # 确保 'date1' 列是日期格式
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filtered_df.reset_index(inplace=True)
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filtered_df["year_month"] = filtered_df["date1"].dt.to_period("M")
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monthly_avg = filtered_df.groupby("year_month")[["Ek", "E_p"]].mean()
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full_range = pd.period_range(
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start=monthly_avg.index.min(), end=monthly_avg.index.max(), freq="M")
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full_monthly_avg = pd.DataFrame(index=full_range).join(monthly_avg)
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full_monthly_avg["Ek"] = pd.to_numeric(
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full_monthly_avg["Ek"], errors="coerce")
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full_monthly_avg["E_p"] = pd.to_numeric(
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full_monthly_avg["E_p"], errors="coerce")
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ax.plot(full_monthly_avg.index.values.astype(
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str), full_monthly_avg["Ek"].values, marker="o", linestyle="-", color="r", label="动能月平均值")
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ax.plot(full_monthly_avg.index.values.astype(
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str), full_monthly_avg["E_p"].values, marker="o", linestyle="-", color="b", label="势能月平均值")
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ax.set_title("动能和势能分布情况", fontsize=24)
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ax.set_xlabel("Month", fontsize=14)
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ax.set_ylabel("Wave energy (J/kg)", fontsize=14)
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months = full_monthly_avg.index.values.astype(str)
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june_indices = [i for i, date in enumerate(months) if date.endswith("-06")]
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december_indices = [i for i, date in enumerate(
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months) if date.endswith("-12")]
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selected_indices = june_indices + december_indices
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ax.set_xticks(ticks=selected_indices, labels=[
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months[i] for i in selected_indices], rotation=45)
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ax.legend()
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# def render_based_on_mode(df, mode, seaon = None):
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def render_based_on_mode(df, mode, season=None):
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buf = BytesIO()
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fig, ax = plt.subplots(figsize=(10, 6))
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if mode == "w/f值统计结果":
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plot_w_f(df, ax)
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elif mode == "周期统计结果":
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plot_zhou_qi(df, ax)
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elif mode == "垂直波长分布":
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plot_ver_wave_len(df, ax)
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elif mode == "水平波长分布":
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plot_hori_wave_len(df, ax)
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elif mode == "纬向本征相速度":
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plot_c_x(df, ax)
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elif mode == "经向本征相速度":
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plot_c_y(df, ax)
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elif mode == "垂直本征相速度":
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plot_c_z(df, ax)
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elif mode == "Zonal wind amplitude (m/s)":
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plot_u1(df, ax)
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elif mode == "扰动振幅统计结果":
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plot_v1(df, ax)
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elif mode == "Temperature amplitude (K)":
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plot_T1(df, ax)
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elif mode == "纬向动量通量统计结果":
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plot_MFu(df, ax)
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elif mode == "经向动量通量统计结果":
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plot_MFv(df, ax)
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elif mode == "horizontal propagation":
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plot_horizontal_propagation(df, season="Fall")
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elif mode == "每月上传/下传重力波占比":
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plot_vertical_propagation(df, ax)
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elif mode == "动能和势能分布情况":
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plot_energy_distribution(df, ax)
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else:
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raise ValueError("Invalid mode")
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plt.rcParams["font.sans-serif"] = ["SimHei"] # 显示中文
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plt.savefig(buf)
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buf.seek(0)
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plt.close()
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return buf
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# if mode == ""
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def get_all_modes():
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return [
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"w/f值统计结果",
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"周期统计结果",
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"垂直波长分布",
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"水平波长分布",
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"纬向本征相速度",
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"经向本征相速度",
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"垂直本征相速度",
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"Zonal wind amplitude (m/s)",
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"扰动振幅统计结果",
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"Temperature amplitude (K)",
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"纬向动量通量统计结果",
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"经向动量通量统计结果",
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"horizontal propagation",
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"每月上传/下传重力波占比",
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"动能和势能分布情况",
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]
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