Add write_t16 function

data/LYN_2023.csv

@@ -1,19 +1,19 @@
-# stationcode LYN,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# latitude deg N 55.79065,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# longitude deg E 12.52509,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# altitude in m amsl 40,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# timezone offset from UTC in hours 1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# time stamp (Year Month Day Hour Minute) reference is in UTC and refers to the end of the period,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# missing data points in GHI DNI and DIF are noted with -999,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# GHI is the global horizontal irradiance in W/m2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# DNI is the direct normal irradiance in W/m2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# DIF is the diffuse horizontal irradiance in W/m2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# GHIcalc is the calculated GHI from DNI and DIF,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# Elev is the solar elevation in deg,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# Azim is the solar azimuth angle in deg N,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# Kc is the clearsky index calculated with CAMS mcclear with GHIcalc/GHI McClear,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# usable is the validity of a data point with 1 being valid and 0 being not usable,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-# flag values from various tests: 1 means the data failed the test. 0 means the test was passed. -999 means the test domain was not met,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+# stationcode LYN
+# latitude deg N 55.79065
+# longitude deg E 12.52509
+# altitude in m amsl 40
+# 
+# time stamp (Year Month Day Hour Minute) is in UTC and refers to the end of the period
+# missing data points in GHI DNI and DIF are noted as empty cells
+# GHI is the global horizontal irradiance in W/m2
+# DNI is the direct normal irradiance in W/m2
+# DIF is the diffuse horizontal irradiance in W/m2
+# GHIcalc is the calculated GHI from DNI and DIF
+# Elev is the solar elevation in deg
+# Azim is the solar azimuth angle in deg N
+# Kc is the clearsky index calculated with CAMS mcclear with GHIcalc/GHI McClear
+# usable is the validity of a data point with 1 being valid and 0 being not usable
+# flag values from various tests: 1 means the data failed the test. 0 means the test was passed.
 Year,Month,Day,Hour,Minute,GHI,DNI,DIF,GHIcalc,Elev,Azim,Kc,usable,flagPPLDIF,flagERLDIF,flagPPLDNI,flagERLDNI,flagPPLGHI,flagERLGHI,flag3highSZA,flag3lowSZA,flagKt,flagKKt,flagKn,flagKnKt,flagKhighSZA,flagKlowSZA,flagTracker,flagManual
 2023,1,1,0,0,-0.1224,-0.06081,-0.16,,,,,,,,,,,,,,,,,,,,,
 2023,1,1,0,1,-0.1176,-0.04698,-0.1671,,,,,,,,,,,,,,,,,,,,,

docs/source/documentation.rst

@@ -18,4 +18,5 @@ Code documentation
    quality.bsrn_limits
    quality.bsrn_limits_flag
    iotools.read_t16
+   iotools.write_t16
    processing.resample_to_freq

src/solarpy/iotools/__init__.py

@@ -1 +1,2 @@
 from solarpy.iotools.read_t16 import read_t16  # noqa: F401
+from solarpy.iotools.write_t16 import write_t16  # noqa: F401

src/solarpy/iotools/read_t16.py

@@ -1,9 +1,20 @@
 import numpy as np
 import pandas as pd
 
+VARIABLE_MAP = {
+    "GHI": "ghi",
+    "DIF": "dhi",
+    "DNI": "dni",
+    "GHI_clear": "ghi_clear",
+    "DIF_clear": "dhi_clear",
+    "DNI_clear": "dni_clear",
+    "BHI_clear": "bhi_clear",
+    "GHI_extra": "ghi_extra",
+    "DNI_extra": "dni_extra",
+}
 
-def read_t16(filename, drop_dates=False, map_variables=False,
-             encoding='utf-8'):
+
+def read_t16(filename, drop_dates=False, map_variables=False, encoding="utf-8"):
     """
     Read a time series data file following the IEA PVPS T16 file format.
 
@@ -30,60 +41,63 @@ def read_t16(filename, drop_dates=False, map_variables=False,
 
     """
     meta = {
-        'stationcode': None,
-        'latitude deg N': np.nan,
-        'longitude deg E': np.nan,
-        'altitude in m amsl': np.nan,
-        'timezone offset from UTC in hours': np.nan,
+        "stationcode": None,
+        "latitude deg N": np.nan,
+        "longitude deg E": np.nan,
+        "altitude in m amsl": np.nan,
     }
 
-    with open(filename, 'r', encoding=encoding) as fbuf:
+    with open(filename, "r", encoding=encoding) as fbuf:
         # Parse through initial metadata section (lines starting with #)
         while True:
             line = fbuf.readline().strip()
 
             # lines with metadata
-            if line.startswith('#'):
-                line = line.lstrip('#')
+            if line.startswith("#"):
+                line = line.lstrip("#")
             # line with column names
             else:
-                names = line.split(',')
+                names = line.split(",")
                 break
 
             for mk in meta.keys():
                 if mk in line:
-                    meta[mk] = line.replace(mk, '').replace(',', '').strip()
+                    meta[mk] = line.replace(mk, "").replace(",", "").strip()
                     # Fix issue in older files
-                    meta[mk] = meta[mk].replace('longitude deg N', 'longitude deg E')
+                    meta[mk] = meta[mk].replace("longitude deg N", "longitude deg E")
 
-        for k in ['latitude deg N', 'longitude deg E', 'altitude in m amsl',
-                  'timezone offset from UTC in hours']:
-            meta[k] = float(meta[k])
-        if meta['stationcode'] == '':
-            meta['stationcode'] = None
+        for k in [
+            "latitude deg N",
+            "longitude deg E",
+            "altitude in m amsl",
+        ]:
+            # convert to float or int
+            meta[k] = float(meta[k]) if "." in meta[k] else int(meta[k])
+        if meta["stationcode"] == "":
+            meta["stationcode"] = None
 
         data = pd.read_csv(
             fbuf,
-            sep=',',
+            sep=",",
             names=names,
-            na_values=[-999, -9999, 'NAN'],
+            na_values=[-999, -9999, "NAN"],
             # Both naming conventions of comment occur
-            dtype={'comments': str, 'Comments': str,
-                   'Remarks': str},
+            dtype={"comments": str, "Comments": str, "Remarks": str},
         )
 
-    datetime_columns = ['Year', 'Month', 'Day', 'Hour', 'Minute']
+    datetime_columns = ["Year", "Month", "Day", "Hour", "Minute"]
 
     data.index = pd.to_datetime(
-        data[datetime_columns].rename(columns=str.lower)).dt.tz_localize('UTC')
+        data[datetime_columns].rename(columns=str.lower)
+    ).dt.tz_localize("UTC")
 
     if drop_dates:
         data = data.drop(columns=datetime_columns)
 
     if map_variables:
-        meta['latitude'] = meta.pop('latitude deg N')
-        meta['longitude'] = meta.pop('longitude deg E')
-        meta['altitude'] = meta.pop('altitude in m amsl')
-        data = data.rename(columns={'GHI': 'ghi', 'DIF': 'dhi', 'DNI': 'dni'})
+        meta["latitude"] = meta.pop("latitude deg N")
+        meta["longitude"] = meta.pop("longitude deg E")
+        meta["altitude"] = meta.pop("altitude in m amsl")
+        data = data.rename(columns=VARIABLE_MAP)
 
     return data, meta

src/solarpy/iotools/write_t16.py

@@ -0,0 +1,72 @@
+import pandas as pd
+
+
+# %%
+def write_t16(filename, data, meta, lines=None, missing=None, encoding="utf-8"):
+    """Write a time series DataFrame to a file in the IEA PVPS T16 format.
+
+    Parameters
+    ----------
+    filename : str or path-like
+        Path to the output file.
+    data : pandas.DataFrame
+        Time series data with a timezone-aware ``DatetimeIndex``; the index
+        is converted to UTC before extracting Year/Month/Day/Hour/Minute.
+        Column names are written as-is to the header row.
+    meta : dict
+        Station metadata. Recognised keys (either short or long form):
+
+        - ``"latitude"`` or ``"latitude deg N"``
+        - ``"longitude"`` or ``"longitude deg E"``
+        - ``"altitude"`` or ``"altitude in m amsl"``
+        - ``"stationcode"`` *(optional)*
+    lines : list of str or None, default ``None``
+        Additional lines written to the header after the location metadata,
+        each prefixed with ``"# "``. If ``None``, no extra lines are written.
+    missing : str or None, default ``None``
+        String written in place of missing (NaN) values. ``None`` leaves the
+        cell empty.
+    encoding : str, default ``"utf-8"``
+        File encoding passed to ``open()``.
+    """
+    latitude = meta.get("latitude", meta.get("latitude deg N"))
+    longitude = meta.get("longitude", meta.get("longitude deg E"))
+    altitude = meta.get("altitude", meta.get("altitude in m amsl"))
+    stationcode = meta.get("stationcode", "")
+
+    utc_index = data.index.tz_convert("UTC")
+    datetime_cols = pd.DataFrame(
+        {
+            "Year": utc_index.year,
+            "Month": utc_index.month,
+            "Day": utc_index.day,
+            "Hour": utc_index.hour,
+            "Minute": utc_index.minute,
+        },
+        index=data.index,
+    )
+
+    # Add time columns and drop existing time columns if present
+    _datetime_col_names = ["Year", "Month", "Day", "Hour", "Minute"]
+    out = pd.concat(
+        [datetime_cols, data.drop(columns=_datetime_col_names, errors="ignore")], axis=1
+    )
+
+    missing_ref = "empty cells" if missing is None else missing
+
+    with open(filename, "w", encoding=encoding) as f:
+        f.write(f"# stationcode {stationcode}\n")
+        f.write(f"# latitude deg N {latitude}\n")
+        f.write(f"# longitude deg E {longitude}\n")
+        f.write(f"# altitude in m amsl {altitude}\n")
+        f.write("# \n")
+        f.write(
+            "# time stamp (Year Month Day Hour Minute) is in UTC and refers to the end of the period\n"  # noqa: E501
+        )
+        f.write(
+            f"# missing data points in GHI DNI and DIF are noted as {missing_ref}\n"
+        )
+        for line in lines or []:
+            f.write(f"# {line}\n")
+        na_rep = "" if missing is None else missing
+        out.to_csv(f, index=False, na_rep=na_rep, lineterminator="\n")

tests/data/LYN_2023_short.csv

@@ -0,0 +1,137 @@
+# stationcode LYN
+# latitude deg N 55.79065
+# longitude deg E 12.52509
+# altitude in m amsl 40
+# 
+# time stamp (Year Month Day Hour Minute) is in UTC and refers to the end of the period
+# missing data points in GHI DNI and DIF are noted as empty cells
+# GHI is the global horizontal irradiance in W/m2
+# DNI is the direct normal irradiance in W/m2
+# DIF is the diffuse horizontal irradiance in W/m2
+# GHIcalc is the calculated GHI from DNI and DIF
+# Elev is the solar elevation in deg
+# Azim is the solar azimuth angle in deg N
+# Kc is the clearsky index calculated with CAMS mcclear with GHIcalc/GHI McClear
+# usable is the validity of a data point with 1 being valid and 0 being not usable
+# flag values from various tests: 1 means the data failed the test. 0 means the test was passed.
+Year,Month,Day,Hour,Minute,GHI,DNI,DIF,GHIcalc,Elev,Azim,Kc,usable,flagPPLDIF,flagERLDIF,flagPPLDNI,flagERLDNI,flagPPLGHI,flagERLGHI,flag3highSZA,flag3lowSZA,flagKt,flagKKt,flagKn,flagKnKt,flagKhighSZA,flagKlowSZA,flagTracker,flagManual
+2023,1,1,0,0,-0.1224,-0.06081,-0.16,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,1,-0.1176,-0.04698,-0.1671,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,2,-0.1302,-0.0529,-0.1765,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,3,-0.1258,-0.05213,-0.1822,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,4,-0.1279,-0.05613,-0.176,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,5,-0.1226,-0.05611,-0.1748,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,6,-0.1292,-0.0573,-0.1777,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,7,-0.1446,-0.0644,-0.194,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,8,-0.1505,-0.06834,-0.2038,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,9,-0.1489,-0.06418,-0.1925,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,10,-0.1351,-0.05588,-0.1899,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,11,-0.1496,-0.05769,-0.1907,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,12,-0.1297,-0.05676,-0.173,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,13,-0.1294,-0.06836,-0.1823,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,14,-0.1366,-0.0683,-0.1923,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,15,-0.1341,-0.05331,-0.1882,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,16,-0.1269,-0.05485,-0.1809,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,17,-0.1135,-0.0658,-0.1593,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,18,-0.1003,-0.06252,-0.1588,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,19,-0.09087,-0.05327,-0.1495,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,20,-0.09391,-0.05008,-0.1526,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,21,-0.1086,-0.05702,-0.1638,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,22,-0.09197,-0.05227,-0.1548,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,23,-0.1174,-0.06043,-0.1766,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,24,-0.1313,-0.06242,-0.1875,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,25,-0.1469,-0.05607,-0.1919,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,26,-0.135,-0.05053,-0.1876,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,27,-0.1307,-0.05136,-0.1888,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,28,-0.1411,-0.0468,-0.2007,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,29,-0.1539,-0.05035,-0.198,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,30,-0.1118,-0.03771,-0.1801,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,31,-0.09743,-0.03728,-0.1605,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,32,-0.1069,-0.03152,-0.1598,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,33,-0.08676,-0.03962,-0.151,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,34,-0.07335,-0.02951,-0.1396,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,35,-0.07482,-0.03063,-0.1287,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,36,-0.08169,-0.03333,-0.1444,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,37,-0.07467,-0.02331,-0.1323,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,38,-0.07664,-0.02949,-0.1383,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,39,-0.06863,-0.01919,-0.131,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,40,-0.06844,-0.0268,-0.1222,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,41,-0.04628,-0.01982,-0.1119,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,42,-0.0556,-0.02655,-0.1112,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,43,-0.05468,-0.03668,-0.1178,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,44,-0.0401,-0.03373,-0.09905,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,45,-0.03023,-0.02554,-0.09702,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,46,-0.0366,-0.02601,-0.09539,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,47,-0.01802,-0.02187,-0.08899,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,48,-0.02152,-0.01562,-0.07897,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,49,-0.01014,-0.01607,-0.07886,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,50,0.0006387,-0.0128,-0.06618,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,51,0.03859,-0.002978,-0.03596,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,52,0.03582,-0.009467,-0.03097,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,53,0.03582,-0.01091,-0.0286,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,54,0.05144,-0.006193,-0.01909,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,55,0.05168,-0.003984,-0.01833,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,56,0.08026,0.007534,-0.005011,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,57,0.1076,-0.006134,0.01878,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,58,0.1096,0.004615,0.02994,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,0,59,0.13,0.005641,0.03861,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,0,0.144,0.01108,0.0483,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,1,0.1256,0.002386,0.04298,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,2,0.1736,0.01215,0.06227,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,3,0.201,0.005937,0.07519,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,4,0.1356,-0.01773,0.0491,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,5,0.09327,-0.02712,0.02312,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,6,0.0713,-0.02649,0.00445,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,7,0.04974,-0.02791,-0.01067,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,8,0.0445,-0.02112,-0.01473,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,9,0.02696,-0.02923,-0.02549,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,10,0.01783,-0.0282,-0.02375,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,11,0.02338,-0.02456,-0.02457,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,12,0.03641,-0.02485,-0.0239,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,13,0.03851,-0.02465,-0.01098,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,14,0.01978,-0.03503,-0.01842,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,15,0.0003484,-0.04424,-0.02838,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,16,-0.02586,-0.04978,-0.04946,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,17,-0.02841,-0.04672,-0.04707,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,18,-0.03366,-0.03915,-0.04635,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,19,-0.02007,-0.03061,-0.05082,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,20,-0.01862,-0.03611,-0.05321,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,21,-0.01256,-0.04047,-0.0508,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,22,-0.02193,-0.04294,-0.04566,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,23,0.03791,-0.0369,-0.0214,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,24,0.033,-0.04075,-0.02048,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,25,-0.01715,-0.06154,-0.03886,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,26,-0.01467,-0.0556,-0.04074,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,27,0.0101,-0.04343,-0.03544,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,28,-0.02195,-0.0638,-0.04819,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,29,0.01115,-0.05773,-0.03255,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,30,0.02313,-0.06491,-0.02757,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,31,0.08371,-0.04925,0.01261,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,32,0.08491,-0.0629,0.02021,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,33,0.1518,-0.05031,0.05708,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,34,0.1679,-0.09516,0.06439,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,35,0.114,-0.1354,0.03958,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,36,0.03259,-0.1865,-0.001248,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,37,0.05268,-0.1483,0.009462,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,38,0.01192,-0.1555,0.000597,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,39,0.01281,-0.1629,-0.008376,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,40,0.105,-0.1159,0.03483,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,41,0.004161,-0.1673,-0.02156,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,42,-0.004529,-0.1523,-0.03088,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,43,0.007897,-0.1414,-0.01478,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,44,0.0126,-0.1185,-0.01549,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,45,0.01732,-0.1138,-0.01773,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,46,-0.03627,-0.1232,-0.03754,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,47,-0.03704,-0.1219,-0.04743,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,48,0.02899,-0.08818,-0.01389,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,49,0.02251,-0.08783,-0.01789,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,50,-0.01386,-0.1061,-0.03528,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,51,0.01094,-0.08465,-0.02511,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,52,0.03718,-0.09704,-0.01192,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,53,-0.007993,-0.1207,-0.03034,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,54,0.03265,-0.09221,-0.007236,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,55,0.04726,-0.1091,-0.003582,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,56,-0.002052,-0.1283,-0.02902,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,57,-0.0209,-0.1218,-0.03445,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,58,-0.01353,-0.09583,-0.04014,,,,,,,,,,,,,,,,,,,,,
+2023,1,1,1,59,0.04343,-0.06507,-0.007833,,,,,,,,,,,,,,,,,,,,,