Eval für 2022 geschrieben. Soweit fertig, muss noch etwas poliert werden

5 months ago · 774887b1bb
7 changed files with 470 additions and 23653 deletions
--- a/.idea/FINO2_data_evaluation.iml
+++ b/.idea/FINO2_data_evaluation.iml
@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.10 (data_evaluation)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (FINO2_data_evaluation)" jdkType="Python SDK" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
 </module>
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (data_evaluation)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (FINO2_data_evaluation)" project-jdk-type="Python SDK" />
  <component name="PyCharmProfessionalAdvertiser">
    <option name="shown" value="true" />
  </component>
--- a/code/calendar_plot.py
+++ b/code/calendar_plot.py
@ -0,0 +1,318 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import math
+import numpy as np
+
+def add_coverage(dataframe, time_list, measurement_variable: str, start_datetime: str,
+                 end_datetime: str, value=1.0):  # format: "2022-12-22 18:00"
+    time_begin = datetime.datetime.strptime(start_datetime, "%Y-%m-%d %H:%M")
+    time_end = datetime.datetime.strptime(end_datetime, "%Y-%m-%d %H:%M")
+    FFF = time_list.index(time_begin)
+    GGG = time_list.index(time_end)
+    dataframe.loc[FFF: GGG, measurement_variable] = value
+    return dataframe
+
+def make_start_df():
+    date1 = '2022-01-01 00:00'
+    date2 = '2022-12-31 23:50'
+    my_dates = pd.date_range(date1, date2, freq="1H").tolist()
+    coverage_df = pd.DataFrame()
+    coverage_df['datetimes']= my_dates
+    return coverage_df, my_dates
+
+
+def get_realtime_from_seconds(seconds):
+    result= datetime.datetime(2000,1,1) + datetime.timedelta(seconds=seconds)
+    print(result)
+    return result
+
+def plot_results(coverage_df):
+    coverage_df= coverage_df.set_index('datetimes')
+    coverage_df.plot()
+    plt.show()
+
+
+
+def get_awac_export(coverage_df,showfig=True):
+    AWAC_seegang_df = pd.read_csv(
+        r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp FINO2 Seegang_klein 2022-01-01-2022-12-28.csv")
+    B = [datetime.datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(AWAC_seegang_df["date"])]
+    AWAC_seegang_df["date"] = B
+    AWAC_redu = AWAC_seegang_df.iloc[range(0, len(AWAC_seegang_df), 6)]
+
+    current_coverage_insida = []
+    wave_coverage_insida = []
+    for element in AWAC_redu['ADCPEW (21.0m)']:
+        if math.isnan(element):
+            current_coverage_insida.append(np.nan)
+        else:
+            current_coverage_insida.append(25.0)
+    for element in AWAC_redu['SG_HS (0.0m)']:
+        if math.isnan(element):
+            wave_coverage_insida.append(np.nan)
+        else:
+            wave_coverage_insida.append(26.0)
+
+    seegang_coverage = pd.DataFrame()
+    seegang_coverage['date']= AWAC_redu['date'].tolist()
+    seegang_coverage['current']=current_coverage_insida
+    seegang_coverage['wave']=wave_coverage_insida
+
+    #add data from old awac
+    seegang_coverage.loc[0:3616,'current']=24.9
+    seegang_coverage.loc[0:3616,'wave']=25.9
+
+    current_counter=0
+    for element in seegang_coverage['current']:
+        if not math.isnan(element):
+            current_counter+=1
+    coverage_current= current_counter / len(seegang_coverage)
+    counter_wave=0
+    for element in seegang_coverage['wave']:
+        if not math.isnan(element):
+            counter_wave+=1
+    coverage_wave= counter_wave / len(seegang_coverage)
+
+    if showfig:
+        plt.figure()
+        plt.plot(seegang_coverage['date'], seegang_coverage['current'], label = 'current')
+        plt.plot(seegang_coverage['date'], seegang_coverage['wave'], label='wave')
+        plt.legend()
+        plt.show()
+
+    coverage_df['wave']=seegang_coverage['wave']
+    coverage_df['current']=seegang_coverage['current']
+
+    return coverage_df, coverage_wave, coverage_current
+
+
+def sami_ph(coverage_df, datetimes):
+    var_name='pH (6m)raw'
+    begin_date="2022-03-02 13:00"
+    end_date="2022-04-24 23:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=5.0)
+    begin_date = "2022-07-03 13:00"
+    end_date = "2022-08-11 19:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=5.0)
+
+    counter_pH=0
+    for element in coverage_df['pH (6m)raw']:
+        if not math.isnan(element):
+            counter_pH+=1
+    coverage_pH= counter_pH / len(coverage_df)
+
+    return coverage_df, coverage_pH
+
+def get_wqm_coverage(coverage_df, datetimes, showfig=True):
+    WQM_1H_df = pd.read_csv(
+        r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp FINO2 WQM_1H 2022-01-01-2022-12-28.csv")
+    B = [datetime.datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(WQM_1H_df["date"])]
+    WQM_1H_df["date"] = B
+
+    counter_2m = 0
+    for element in WQM_1H_df['CHL (2.0m)']:
+        if not math.isnan(element):
+            counter_2m += 1
+    coverage_2m = counter_2m / len(WQM_1H_df)
+
+    counter_12m = 0
+    for element in WQM_1H_df['CHL (12.0m)']:
+        if not math.isnan(element):
+            counter_12m += 1
+    coverage_12m = counter_12m / len(WQM_1H_df)
+
+    counter_20m = 0
+    for element in WQM_1H_df['CHL (20.0m)']:
+        if not math.isnan(element):
+            counter_20m += 1
+    coverage_20m = counter_20m / len(WQM_1H_df)
+
+    wqm2m_coverage_insida = []
+    for element in WQM_1H_df['CHL (2.0m)']:
+        if math.isnan(element):
+            wqm2m_coverage_insida.append(np.nan)
+        else:
+            wqm2m_coverage_insida.append(-2.0)
+    wqm12m_coverage_insida = []
+    for element in WQM_1H_df['CHL (12.0m)']:
+        if math.isnan(element):
+            wqm12m_coverage_insida.append(np.nan)
+        else:
+            wqm12m_coverage_insida.append(-12.0)
+    wqm20m_coverage_insida = []
+    for element in WQM_1H_df['CHL (20.0m)']:
+        if math.isnan(element):
+            wqm20m_coverage_insida.append(np.nan)
+        else:
+            wqm20m_coverage_insida.append(-20.0)
+
+    coverage_wqm = {
+        "2m":coverage_2m,
+        "12m":coverage_12m,
+        "20m":coverage_20m
+    }
+
+    coverage_df['WQM_2m'] = np.nan
+    coverage_df['WQM_12m'] = np.nan
+    coverage_df['WQM_20m'] = np.nan
+    coverage_df.loc[0:len(wqm2m_coverage_insida)-1,'WQM_2m'] = wqm2m_coverage_insida
+    coverage_df.loc[0:len(wqm2m_coverage_insida)-1,'WQM_12m'] = wqm12m_coverage_insida
+    coverage_df.loc[0:len(wqm2m_coverage_insida)-1,'WQM_20m'] = wqm20m_coverage_insida
+
+    if showfig:
+        plt.figure()
+        plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (2.0m)'], label="CHL 2m")
+        plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (12.0m)'], label="CHL 12m")
+        plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (20.0m)'], label="CHL 20m")
+        plt.show()
+
+    return coverage_df, coverage_wqm
+
+
+def get_mc_coverage(coverage_df, datetimes, showfig=True):
+    CTD_nurDruck_df = pd.read_csv(
+        r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp_FINO2 D 2022-01-01-2022-12-28.csv")
+    B = [datetime.datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(CTD_nurDruck_df["date"])]
+    CTD_nurDruck_df["date"] = B
+
+
+    coverage_mc={
+        "2m":0.0,
+        "4m":0.0,
+        "6m":0.0,
+        "8m":0.0,
+        "10m":0.0,
+        "12m":0.0,
+        "14m":0.0,
+        "16m":0.0,
+        "18m":0.0,
+        "20m":0.0,
+    }
+    get_colmns = ['WP (2.0m)', 'WP (4.0m)', 'WP (6.0m)', 'WP (8.0m)', 'WP (10.0m)', 'WP (12.0m)', 'WP (14.0m)', 'WP (16.0m)', 'WP (18.0m)', 'WP (20.0m)']
+    get_dict_posi= ['2m', '4m', '6m', '8m', '10m', '12m', '14m', '16m', '18m', '20m']
+    set_colmns = ['MC_2m', 'MC_4m', 'MC_6m', 'MC_8m', 'MC_10m', 'MC_12m', 'MC_14m', 'MC_16m', 'MC_18m', 'MC_20m']
+    values = [x + 0.0 for x in [2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0]]
+
+
+    for idx,ele in enumerate(set_colmns):
+        coverage_df[ele] = np.nan
+        temp_cover = []
+        for element in CTD_nurDruck_df[get_colmns[idx]]:
+            if math.isnan(element):
+                temp_cover.append(np.nan)
+            else:
+                temp_cover.append(values[idx])
+        coverage_df.loc[0:len(temp_cover) - 1, set_colmns[idx]] = temp_cover
+
+
+
+    # 2m MC raw
+    var_name= "MC (2m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=1.8)
+    # 4m MC raw
+    var_name = "MC (4m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-04-10 02:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=3.8)
+    begin_date = "2022-05-31 13:00"
+    end_date = "2022-10-06 00:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=3.8)
+    # 6m MC raw
+    var_name = "MC (6m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-05-08 13:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=5.8)
+    begin_date = "2022-05-31 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=5.8)
+    # 8m MC raw
+    var_name = "MC (8m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=7.8)
+    # 10m MC raw
+    var_name = "MC (10m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=9.8)
+    # 12m MC raw
+    var_name = "MC (12m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=11.8)
+    # 14m MC raw
+    var_name = "MC (14m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=13.8)
+
+    # 16m MC raw
+    var_name = "MC (16m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-05-16 11:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=15.8)
+    begin_date = "2022-06-10 08:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=15.8)
+
+    # 18m MC raw
+    var_name = "MC (18m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=17.8)
+    # 20m MC raw
+    var_name = "MC (20m)raw"
+    begin_date = "2022-03-02 13:00"
+    end_date = "2022-10-20 06:00"
+    coverage_df = add_coverage(coverage_df, datetimes, var_name, begin_date, end_date, value=19.8)
+
+
+    # -------------------
+
+    i1=['MC_2m', 'MC_4m', 'MC_6m', 'MC_8m', 'MC_10m', 'MC_12m', 'MC_14m', 'MC_16m', 'MC_18m', 'MC_20m']
+    i2=['MC (2m)raw', 'MC (4m)raw', 'MC (6m)raw', 'MC (8m)raw', 'MC (10m)raw', 'MC (12m)raw', 'MC (14m)raw', 'MC (16m)raw', 'MC (18m)raw', 'MC (20m)raw']
+    i3=['MC_2m_combi', 'MC_4m_combi', 'MC_6m_combi', 'MC_8m_combi', 'MC_10m_combi', 'MC_12m_combi', 'MC_14m_combi', 'MC_16m_combi', 'MC_18m_combi', 'MC_20m_combi']
+    values=[x+0.2 for x in[2.0,4.0,6.0,8.0,10.0,12.0,14.0,16.0,18.0,20.0]]
+
+    for ixd,step in enumerate(i1):
+        temp_list = []
+        for ind, element in enumerate(coverage_df[step]):
+            if not math.isnan(element):
+                temp_list.append(values[ixd])
+            elif not math.isnan(coverage_df[i2[ixd]][ind]):
+                temp_list.append(values[ixd])
+            else:
+                temp_list.append(np.nan)
+        coverage_df[i3[ixd]]=temp_list
+
+    for idx,ele in enumerate(get_colmns):
+        temp_counter = 0
+        for element in CTD_nurDruck_df[get_colmns[idx]]:
+            if not math.isnan(element):
+                temp_counter += 1
+        coverage_mc[get_dict_posi[idx]] = temp_counter / len(CTD_nurDruck_df)
+
+
+    return coverage_df, coverage_mc
+
+
+
+
+if __name__ == '__main__':
+
+
+    coverage_df, datetimes = make_start_df()
+    coverage_df, coverage_wave, coverage_current = get_awac_export(coverage_df, showfig=False)
+    coverage_df, coverage_pH = sami_ph(coverage_df, datetimes)
+    coverage_df, coverage_wqm  = get_wqm_coverage(coverage_df, datetimes, showfig=False)
+    coverage_df, coverage_mc = get_mc_coverage(coverage_df, datetimes, showfig=True)
+    plot_results(coverage_df)
+    print("")
+    print("pH coverage: " + str(coverage_pH))
+    print("wave coverage: " + str(coverage_wave))
+    print("current coverage: " + str(coverage_current))
+    print("WQM coverage (2,12,20m): " + str(coverage_wqm))
+    print("MC coverage (2-20m): " + str(coverage_mc))
--- a/code/endbericht_awac_direktauslese.py
+++ b/code/endbericht_awac_direktauslese.py
@ -1,8 +1,6 @@
 from datetime import datetime
 import glob
 import pandas as pd
-import plotly.graph_objects as go
-import plotly.express as px


 def load_csv(file_path_in):
@ -19,16 +17,12 @@ def load_csv(file_path_in):
    cols = awac_data_df.columns.tolist()
    cols = cols[-1:] + cols[:-1]
    awac_data_df = awac_data_df[cols]
-
-
+    awac_raw_data_2022_df = awac_data_df.loc[12423:16035, ['DateTime', 'Speed#20(21.7m)']]


    #y=['Pressure','Heading','Pitch','Battery', 'Speed#1(2.8m)','Speed#2(3.8m)','Speed#3(4.7m)' ]
    y=awac_data_list[0][40:-1]

-    fig = px.line(awac_data_df, x="DateTime", y=y, markers="x", range_x= [12423,16200])
-    fig.show()
-

    print("")

--- a/code/endbericht_microcats_direktauslese.py
+++ b/code/endbericht_microcats_direktauslese.py
@ -1,14 +1,20 @@
-from datetime import datetime
+
+import datetime
+import numpy as np
+import time
 import glob
 import pandas as pd
-import plotly.graph_objects as go
-import plotly.express as px
+import matplotlib.pyplot as plt
+
+
+def secondsSince2000_toDatetime(ss2000):
+    return datetime.datetime(2000,1,1)+ datetime.timedelta(seconds =ss2000)


 def load_cnv(file_path_in):
    with open(file_path_in) as f:
        raw_data = f.readlines()
-    AAA = raw_data[293:]
+    AAA = raw_data[300:]
    BBB = [i.strip().split(" ") for i in AAA]
    CTD_data =[]

@ -16,40 +22,27 @@ def load_cnv(file_path_in):
        CCC = [i.strip() for i in line]
        CTD_data.append([g for g in CCC if g.strip()])

-    CTD_data_df= pd.DataFrame(CTD_data, columns=['C','T','D','JulianDays','unknown']).apply(pd.to_numeric)

+    if len(CTD_data[0])==5:
+        CTD_data_df= pd.DataFrame(CTD_data, columns=['C','T','D','JulianDays','unknown']).apply(pd.to_numeric)
+    elif len(CTD_data[0])==8:
+        CTD_data_df = pd.DataFrame(CTD_data, columns=['seconds_since_2000','T', 'C', 'D', 'sal', 'oxsat','density','flag']).apply(pd.to_numeric)
+        CTD_data_df['date']=CTD_data_df['seconds_since_2000'].apply(secondsSince2000_toDatetime)

    return CTD_data_df


-def plotstuff(ctd_raw_data):
-    """y = ['C']
-    line_plots=[
-        go.Line(x=ctd_raw_data["2m"]['JulianDays'], y=ctd_raw_data['2m'][y], name='2m'),
-        go.Line(x=ctd_raw_data["4m"]['JulianDays'], y=ctd_raw_data['4m'][y], name='4m')
-    ]
-    fig = go.Figure(data=line_plots)
-    fig.show()"""
-
-    y=['D']
-    fig = px.line(ctd_raw_data['2m'], x="JulianDays", y=y)
-
-    fig.add_scatter(ctd_raw_data['4m'], x="JulianDays", y=y)
-    fig.show()
-
-    print("")
-


 def main():
    ctd_raw_data = {}
-    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\2m\SBE37-IM_03709795_2022_10_28.cnv"
+    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\02m\SBE37-IM_03709795_2022_10_28.cnv"
    ctd_raw_data["2m"] = load_cnv(file_path_in)
-    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\4m\SBE37-IM_03709796_2022_10_28.cnv"
+    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\04m\SBE37-IM_03709796_2022_10_28.cnv"
    ctd_raw_data["4m"] = load_cnv(file_path_in)
-    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\6m\SBE37-IM_03709797_2022_10_28.cnv"
+    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\06m\SBE37-IM_03709797_2022_10_28.cnv"
    ctd_raw_data["6m"] = load_cnv(file_path_in)
-    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\8m\SBE37-IM_03709798_2022_10_28.cnv"
+    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\08m\SBE37-IM_03709798_2022_10_28.cnv"
    ctd_raw_data["8m"] = load_cnv(file_path_in)
    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\10m\SBE37-IM_03709799_2022_10_28.cnv"
    ctd_raw_data["10m"] = load_cnv(file_path_in)
@ -64,7 +57,20 @@ def main():
    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\20m\SBE37-IM_03709804_2022_10_28.cnv"
    ctd_raw_data["20m"] = load_cnv(file_path_in)

-    plotstuff(ctd_raw_data)
+    #plotstuff(ctd_raw_data)
+
+    fig3 = plt.figure()
+    plt.plot(ctd_raw_data['2m']['date'], ctd_raw_data['2m']['D'], label="2m")
+    plt.plot(ctd_raw_data['4m']['date'], ctd_raw_data['4m']['D'], label="4m")
+    plt.plot(ctd_raw_data['6m']['date'], ctd_raw_data['6m']['D'], label="6m")
+    plt.plot(ctd_raw_data['8m']['date'], ctd_raw_data['8m']['D'], label="8m")
+    plt.plot(ctd_raw_data['10m']['date'], ctd_raw_data['10m']['D'], label="10m")
+    plt.plot(ctd_raw_data['12m']['date'], ctd_raw_data['12m']['D'], label="12m")
+    plt.plot(ctd_raw_data['14m']['date'], ctd_raw_data['14m']['D'], label="14m")
+    plt.plot(ctd_raw_data['16m']['date'], ctd_raw_data['16m']['D'], label="16m")
+    plt.plot(ctd_raw_data['18m']['date'], ctd_raw_data['18m']['D'], label="18m")
+    plt.plot(ctd_raw_data['20m']['date'], ctd_raw_data['20m']['D'], label="20m")
+    plt.show()


 if __name__ == '__main__':
--- a/code/plot_insida_exports.py
+++ b/code/plot_insida_exports.py
@ -0,0 +1,116 @@
+from datetime import datetime
+import pandas as pd
+import matplotlib.pyplot as plt
+import math
+import numpy as np
+
+
+def plot_MC_export(CTD_nurDruck_df):
+     fig = plt.figure()
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (2.0m)'], label="2m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (4.0m)'], label="4m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (6.0m)'], label="6m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (8.0m)'], label="8m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (10.0m)'], label="10m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (12.0m)'], label="12m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (14.0m)'], label="14m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (16.0m)'], label="16m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (18.0m)'], label="18m")
+     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (20.0m)'], label="20m")
+     plt.show()
+
+
+def plot_WQM_1h_export(WQM_1H_df):
+     plt.figure()
+     plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (2.0m)'], label="CHL 2m")
+     plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (12.0m)'], label="CHL 12m")
+     plt.plot(WQM_1H_df['date'], WQM_1H_df['CHL (20.0m)'], label="CHL 20m")
+     plt.show()
+
+def plot_WQM_10min_export(WQM_10m_df):
+     plt.figure()
+     plt.plot(WQM_10m_df['date'], WQM_10m_df['OX_MCODO (2.0m)'], label="OX 2m")
+     plt.plot(WQM_10m_df['date'], WQM_10m_df['OX_MCODO (12.0m)'], label="OX 12m")
+     plt.plot(WQM_10m_df['date'], WQM_10m_df['OX_MCODO (20.0m)'], label="OX 20m")
+     plt.show()
+
+
+def load_export_data():
+     print("loading data...")
+     AWAC_seegang_df = pd.read_csv(
+          r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp FINO2 Seegang_klein 2022-01-01-2022-12-28.csv")
+     CTD_df = pd.read_csv(
+          r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp_FINO2 CTD 2021-08-01-2022-11-09 1667984812085.csv")
+     CTD_nurDruck_df = pd.read_csv(
+          r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp_FINO2 D 2022-01-01-2022-12-28.csv")
+     WQM_1H_df = pd.read_csv(
+          r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp FINO2 WQM_1H 2022-01-01-2022-12-28.csv")
+     WQM_10m_df = pd.read_csv(
+          r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\Insida_exports\Insida_exp_FINO2_10m O2 2022-01-01-2022-12-28.csv")
+
+     B = [datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(AWAC_seegang_df["date"])]
+     AWAC_seegang_df["date"] = B
+
+     B = [datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(CTD_df["date"])]
+     CTD_df["date"] = B
+
+     B = [datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(CTD_nurDruck_df["date"])]
+     CTD_nurDruck_df["date"] = B
+
+     B = [datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(WQM_1H_df["date"])]
+     WQM_1H_df["date"] = B
+
+     B = [datetime.strptime(a, '%Y-%m-%d %H:%M:%S') for a in list(WQM_10m_df["date"])]
+     WQM_10m_df["date"] = B
+
+     return AWAC_seegang_df,CTD_df,CTD_nurDruck_df,WQM_1H_df,WQM_10m_df
+
+
+def plot_seegang_export(AWAC_seegang_df):
+     BB= range(0,52127,6)
+     AWAC_redu = AWAC_seegang_df.iloc[BB]
+
+
+     current_data=[]
+     wave_data=[]
+     x_axis= range(0,len(AWAC_redu))
+     for element in AWAC_redu['ADCPEW (21.0m)']:
+          if math.isnan(element):
+               current_data.append(np.nan)
+          else:
+               current_data.append(1)
+     for element in AWAC_redu['SG_HS (0.0m)']:
+          if math.isnan(element):
+               wave_data.append(np.nan)
+          else:
+               wave_data.append(2)
+
+     plt.figure()
+     plt.plot(AWAC_redu['date'],wave_data)
+     plt.plot(AWAC_redu['date'],current_data)
+     plt.show()
+
+
+     plt.figure()
+
+     plt.plot(AWAC_redu['date'], AWAC_redu['ADCPEW (21.0m)'], label="ADCPEW 21m")
+     #plt.plot(AWAC_redu['date'], AWAC_redu['SG_DirTp (0.0m)'], label="SG_DirTp")
+     #plt.plot(AWAC_redu['date'], AWAC_redu['SG_TP (0.0m)'], label="SG_TP")
+     plt.plot(AWAC_redu['date'], AWAC_redu['SG_HS (0.0m)'], label="SG_HS")
+
+     plt.legend()
+     plt.show()
+
+
+if __name__ == '__main__':
+     AWAC_seegang_df,CTD_df,CTD_nurDruck_df,WQM_1H_df,WQM_10m_df = load_export_data()
+
+     plot_seegang_export(AWAC_seegang_df)
+
+     #plot_MC_export(CTD_nurDruck_df)
+
+     #plot_WQM_1h_export(WQM_1H_df)
+
+     #plot_WQM_10min_export(WQM_10m_df)
+
+     print("")
--- a/data/evaluation_data_2022/FINO2
+++ b/data/evaluation_data_2022/FINO2