diff --git a/Makefile b/Makefile
index b914a01c7069b92cb727e87a55fb8c6d4cd35d5c..792309de70c434184adb2b69d6dc3e9c84fb995d 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,5 @@
PKGNAME=orcasong
ALLNAMES = $(PKGNAME)
-ALLNAMES += orcasong_contrib
install:
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index f48213457d510ccc80e9db33c7ea3ba8e8a74a4b..fd0babb55508d32ba7aac79a541e5e7066d26773 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -41,6 +41,10 @@ See :ref:`concatenate` for details.
X runs for your training set. Instead, choose runs randomly over
the whole period.
+.. note::
+ For mixing e.g. neutrinos and muon, a list with all DL files that should
+ go into one specific file
+ can be produced with :ref:`make_data_split`.
Step 4: Shuffle
---------------
diff --git a/docs/tools.rst b/docs/tools.rst
index 3f37388d43ede52c21637765cb7f10fc30276bb4..022f9809cab5d1ec0ad1467ebdbb54699bd9caab 100644
--- a/docs/tools.rst
+++ b/docs/tools.rst
@@ -3,6 +3,29 @@ OrcaSong Tools
Orcasong comes with some tools to further process data.
+.. _make_data_split:
+
+Make_data_split
+---------------
+
+Create datasets for different tasks (like classification or regression) from the files
+resulting from OrcaSong, based on the run_id. This is particularly helpful
+for a run-by-run data analysis or to generate equally large datasets per class.
+A toml config is used, in which the directories and ranges of runs to be considered
+can be specified, as well as the subdivision into training and validation sets.
+Detailed descriptions for the options available can be found in examples/make_data_split_config.toml.
+As output, a list in txt format with
+the filepaths belonging to one set is created that can be passed to the concatenate
+for creating one single file out of the many.
+
+In fact, with the option make_qsub_bash_files, scripts for the concatenation
+and shuffle, to be directly submitted on computing clusters, are created.
+
+Can be used via the commandline::
+
+ orcasong/tools/make_data_split.py config.toml
+
+
.. _concatenate:
Concatenate
@@ -10,7 +33,7 @@ Concatenate
Concatenate files resulting from OrcaSong, i.e. merge some h5 files
into a single, bigger one. The resulting file can still be read in with
-km3pipe.
+km3pipe. The input can also be a txt file like from make_data_split.
Can be used via the commandline like so::
@@ -21,8 +44,11 @@ or import as:
.. code-block:: python
from orcasong.tools import FileConcatenator
-
-
+
+ fc = FileConcatenator.from_list(input_file_list_from_make_data_split.txt)
+
+ fc.concatenate(output_filepath_concat)
+
.. _shuffle:
Shuffle
@@ -39,4 +65,8 @@ or import function for general postprocessing:
.. code-block:: python
from orcasong.tools.postproc import postproc_file
+
+ postproc_file(output_filepath_concat)
+
+Theres also a faster (beta) version available called h5shuffle2.
diff --git a/examples/make_data_split_config.toml b/examples/make_data_split_config.toml
new file mode 100644
index 0000000000000000000000000000000000000000..e654dd67de2cdf568158eac237bfa93731444cc0
--- /dev/null
+++ b/examples/make_data_split_config.toml
@@ -0,0 +1,83 @@
+# Example configuration file for make_data_split.py
+
+# --- Documentation for every config parameter that is available --- #
+#
+# Main Parameters
+# ----------
+# n_files_train : int
+# Into how many files the training dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# n_files_validate : int
+# Into how many files the validation dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# n_files_rest : int
+# Into how many files the "rest" dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# output_file_folder : str
+# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
+# output_file_name : str
+# String, that specifies the prefix of the filename of the output .list files.
+# E.g. if = "xyzc_tight_0":
+# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
+# print_only : bool
+# If only informationa about the input_groups should be printed, and no .list files should be made.
+# shuffle_delete : bool
+# True = the input file that will be deleted after the shuffling is finished.
+# Option for the shuffle_h5 tool.
+# venv_path : str
+# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
+# make_qsub_bash_files : bool
+# true = Makes the cluster submission bash files needed to actually
+# concatenate the files in the list files.
+
+
+# Input Group Parameters
+# ----------------------
+# dir : str
+# Path of the directory, where the files for this input group are located.
+# run_ids_train/run_ids_validate/run_ids_rest : array
+# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
+# dataset of this input group.
+# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
+# to the training/validation/rest dataset.
+# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
+#
+# --- Documentation for every config parameter that is available --- #
+
+# --- Main options ---#
+
+n_files_train = 3
+n_files_validate = 1
+n_files_rest = 0
+
+output_file_folder = "/sps/km3net/users/guderian/NN_stuff/split_data_output/ORCA4/graph/ts/"
+
+output_file_name = "test_list"
+
+print_only = false # only print information of your input_groups, don't make any .list files
+
+shuffle_delete = true
+
+venv_path = "/sps/km3net/users/guderian/NN_stuff/deep_learning_source/venv_song/bin/"
+
+make_qsub_bash_files = true
+
+# --- Main options ---#
+
+
+# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
+
+
+[elec_cc]
+dir = "/sps/km3net/users/guderian/NN_stuff/graphs/ORCA4/base/gsg_elecCC-CC_1-500GeV.km3sim/test/"
+run_ids_train = [6763, 6767]
+run_ids_validate = [6768, 6769]
+
+
+[muon_nc]
+dir = "/sps/km3net/users/guderian/NN_stuff/graphs/ORCA4/base/gsg_muonNC-NC_1-500GeV.km3sim/test/"
+run_ids_train = [6763, 6767]
+run_ids_validate = [6768, 6769]
+
+
+# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong/extractors.py b/orcasong/extractors.py
index c636178438f7e6dfafe0747ce0ab53d3baa144cd..1fbc00f5876cb862b4380d9d336b863e47945b94 100644
--- a/orcasong/extractors.py
+++ b/orcasong/extractors.py
@@ -166,7 +166,7 @@ def get_real_data_info_extr(input_file):
# check if std reco is present
f = File(input_file, "r")
has_std_reco = "reco" in f.keys()
-
+
def mc_info_extr(blob):
"""
@@ -231,6 +231,7 @@ def get_random_noise_mc_info_extr(input_file):
# check if std reco is present
f = File(input_file, "r")
has_std_reco = "reco" in f.keys()
+
def mc_info_extr(blob):
@@ -296,7 +297,8 @@ def get_neutrino_mc_info_extr(input_file):
# get the n_gen
header = HDF5Header.from_hdf5(input_file)
n_gen = header.genvol.numberOfEvents
-
+
+
def mc_info_extr(blob):
"""
@@ -368,6 +370,7 @@ def get_neutrino_mc_info_extr(input_file):
"weight_w3": weight_w3,
"n_gen": n_gen,
}
+
# get all the std reco info
if has_std_reco:
@@ -380,7 +383,7 @@ def get_neutrino_mc_info_extr(input_file):
return mc_info_extr
-def get_muon_mc_info_extr(input_file):
+def get_muon_mc_info_extr(input_file,prod_identifier=2):
"""
Wrapper function that includes the actual mc_info_extr
@@ -390,6 +393,9 @@ def get_muon_mc_info_extr(input_file):
----------
input_file : km3net data file
Can be online or offline format.
+ prod_identifier : int
+ Solotion for now: just give a 1 for km3sim and a 2 for JSerine production.
+ They have different simulated run times, shich are needed for the correct scaling.
Returns
-------
@@ -404,7 +410,7 @@ def get_muon_mc_info_extr(input_file):
# no n_gen here, but needed for concatenation
n_gen = 1
-
+
def mc_info_extr(blob):
"""
@@ -434,11 +440,10 @@ def get_muon_mc_info_extr(input_file):
mc_track = blob["McTracks"][p]
- particle_type = (
- mc_track.type
- ) # assumed that this is the same for all muons in a bundle
- is_cc = mc_track.cc # always 0 actually
- bjorkeny = mc_track.by
+ particle_type = mc_track.type # assumed that this is the same for all muons in a bundle
+ is_cc = 0 #set to 0
+ bjorkeny = 0 #set to zero
+
time_interaction = mc_track.time # same for all muons in a bundle
# sum up the energy of all muons
@@ -485,6 +490,7 @@ def get_muon_mc_info_extr(input_file):
"weight_w2": weight_w2,
"weight_w3": weight_w3,
"n_gen": n_gen,
+ "prod_identifier": prod_identifier,
}
# get all the std reco info
diff --git a/orcasong/tools/make_data_split.py b/orcasong/tools/make_data_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..83798625362cb2d56cd9a2301dd8bbfc97e23d95
--- /dev/null
+++ b/orcasong/tools/make_data_split.py
@@ -0,0 +1,471 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+
+__author__ = "Michael Moser, Daniel Guderian"
+
+import os
+import toml
+import argparse
+import h5py
+import random
+import numpy as np
+
+
+def get_parser():
+ parser = argparse.ArgumentParser(
+ description="Create datasets based on the run_id's."
+ "Use the config to add input folder and set the ranges."
+ "Outputs a list in an txt file that can be used to "
+ "concatenate the files specfied"
+ )
+ parser.add_argument(
+ "config", type=str, help="See example config for detailed information"
+ )
+
+ return parser
+
+
+def get_all_ip_group_keys(cfg):
+ """
+ Gets the keys of all input groups in the config dict.
+
+ The input groups are defined as the dict elements, where the values have the type of a dict.
+
+ Parameters
+ ----------
+ cfg : dict
+ Dict that contains all configuration options and additional information.
+
+ Returns
+ -------
+ ip_group_keys : list
+ List of the input_group keys.
+
+ """
+ ip_group_keys = []
+ for key in cfg:
+ if type(cfg[key]) == dict:
+ ip_group_keys.append(key)
+
+ return ip_group_keys
+
+
+def get_h5_filepaths(dirpath):
+ """
+ Returns the filepaths of all .h5 files that are located in a specific directory.
+
+ Parameters
+ ----------
+ dirpath: str
+ Path of the directory where the .h5 files are located.
+
+ Returns
+ -------
+ filepaths : list
+ List with the full filepaths of all .h5 files in the dirpath folder.
+
+ """
+ filepaths = []
+ for f in os.listdir(dirpath):
+ if f.endswith(".h5"):
+ filepaths.append(dirpath + "/" + f)
+
+ # randomize order
+ random.Random(42).shuffle(filepaths)
+
+ return filepaths
+
+
+def get_number_of_evts(file, dataset_key="y"):
+ """
+ Returns the number of events of a file looking at the given dataset.
+
+ Parameters
+ ----------
+ file : h5 file
+ File to read the number of events from.
+ dataset_key : str
+ String which specifies, which dataset in a h5 file should be used for calculating the number of events.
+
+ Returns
+ -------
+ n_evts : int
+ The number of events in that file.
+
+ """
+
+ f = h5py.File(file, "r")
+ dset = f[dataset_key]
+ n_evts = dset.shape[0]
+ f.close()
+
+ return n_evts
+
+
+def get_number_of_evts_and_run_ids(
+ list_of_files, dataset_key="y", run_id_col_name="run_id"
+):
+ """
+ Gets the number of events and the run_ids for all hdf5 files in the list_of_files.
+
+ The number of events is calculated based on the dataset, which is specified with the dataset_key parameter.
+
+ Parameters
+ ----------
+ list_of_files : list
+ List which contains filepaths to h5 files.
+ dataset_key : str
+ String which specifies, which dataset in a h5 file should be used for calculating the number of events.
+ run_id_col_name : str
+ String, which specifies the column name of the 'run_id' column.
+
+ Returns
+ -------
+ total_number_of_evts : int
+ The cumulative (total) number of events.
+ mean_number_of_evts_per_file : float
+ The mean number of evts per file.
+ run_ids : list
+ List containing the run_ids of the files in the list_of_files.
+
+ """
+
+ total_number_of_evts = 0
+ run_ids = []
+
+ for i, fpath in enumerate(list_of_files):
+ f = h5py.File(fpath, "r")
+
+ dset = f[dataset_key]
+ n_evts = dset.shape[0]
+ total_number_of_evts += n_evts
+
+ run_id = f[dataset_key][0][run_id_col_name]
+ run_ids.append(run_id)
+
+ f.close()
+
+ mean_number_of_evts_per_file = total_number_of_evts / len(list_of_files)
+
+ return total_number_of_evts, mean_number_of_evts_per_file, run_ids
+
+
+def split(a, n):
+ """
+ Splits a list into n equal sized (if possible! if not, approximately) chunks.
+
+ Parameters
+ ----------
+ a : list
+ A list that should be split.
+ n : int
+ Number of times the input list should be split.
+
+ Returns
+ -------
+ a_split : list
+ The input list a, which has been split into n chunks.
+
+ """
+ # from https://stackoverflow.com/questions/2130016/splitting-a-list-into-n-parts-of-approximately-equal-length
+ k, m = divmod(len(a), n)
+ a_split = list(
+ (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))
+ )
+ return a_split
+
+
+def print_input_statistics(cfg, ip_group_keys):
+ """
+ Prints some useful information for each input_group.
+
+ Parameters
+ ----------
+ cfg : dict
+ Dict that contains all configuration options and additional information.
+ ip_group_keys : list
+ List of the input_group keys.
+
+ """
+
+ print("----------------------------------------------------------------------")
+ print("Printing input statistics for your " + cfg["toml_filename"] + " input:")
+ print("----------------------------------------------------------------------")
+
+ print(
+ "Your input .toml file has the following data input groups: "
+ + str(ip_group_keys)
+ )
+ print("Total number of events: " + str(cfg["n_evts_total"]))
+
+ for key in ip_group_keys:
+ print("--------------------------------------------------------------------")
+ print("Info for group " + key + ":")
+ print("Directory: " + cfg[key]["dir"])
+ print("Total number of files: " + str(cfg[key]["n_files"]))
+ print("Total number of events: " + str(cfg[key]["n_evts"]))
+ print(
+ "Mean number of events per file: "
+ + str(round(cfg[key]["n_evts_per_file_mean"], 3))
+ )
+ print("--------------------------------------------------------------------")
+
+
+def add_fpaths_for_data_split_to_cfg(cfg, key):
+ """
+ Adds all the filepaths for the output files into a list, and puts them into the cfg['output_dsplit'][key] location
+ for all dsplits (train, validate, rest).
+
+ Parameters
+ ----------
+ cfg : dict
+ Dict that contains all configuration options and additional information.
+ key : str
+ The key of an input_group.
+
+ """
+
+ fpath_lists = {"train": [], "validate": [], "rest": []}
+ for i, fpath in enumerate(cfg[key]["fpaths"]):
+
+ run_id = cfg[key]["run_ids"][i]
+
+ for dsplit in ["train", "validate", "rest"]:
+ if "run_ids_" + dsplit in cfg[key]:
+ if (
+ cfg[key]["run_ids_" + dsplit][0]
+ <= run_id
+ <= cfg[key]["run_ids_" + dsplit][1]
+ ):
+ fpath_lists[dsplit].append(fpath)
+
+ for dsplit in ["train", "validate", "rest"]:
+ if len(fpath_lists[dsplit]) == 0:
+ continue
+
+ n_files_dsplit = cfg["n_files_" + dsplit]
+ fpath_lists[dsplit] = split(fpath_lists[dsplit], n_files_dsplit)
+ if "output_" + dsplit not in cfg:
+ cfg["output_" + dsplit] = dict()
+ cfg["output_" + dsplit][key] = fpath_lists[dsplit]
+
+
+def make_dsplit_list_files(cfg):
+ """
+ Writes .list files of the datasplits to the disk, with the information in the cfg['output_dsplit'] dict.
+
+ Parameters
+ ----------
+ cfg : dict
+ Dict that contains all configuration options and additional information.
+
+ """
+ # check if //conc_list_files folder exists, if not create it.
+ if not os.path.exists(cfg["output_file_folder"] + "/conc_list_files"):
+ os.makedirs(cfg["output_file_folder"] + "/conc_list_files")
+
+ print()
+ print()
+ print("In an run-by-run MC the run_id's might not be continuous.")
+ print("Here are the actual numbers in the split sets:")
+ print("----------------------------------------------")
+
+ # loop over the different specified sets
+ for dsplit in ["train", "validate", "rest"]:
+
+ if "output_" + dsplit not in cfg:
+ continue
+
+ print(dsplit, "set:")
+
+ first_key = list(cfg["output_" + dsplit].keys())[0]
+ n_output_files = len(cfg["output_" + dsplit][first_key])
+
+ # initialize counter of events for all input groups
+ imput_groups_dict = cfg["output_" + dsplit]
+ final_number_of_events = np.zeros(len(imput_groups_dict))
+
+ # loop over the number of outputfiles for each set
+ for i in range(n_output_files):
+ fpath_output = (
+ cfg["output_file_folder"]
+ + "/conc_list_files/"
+ + cfg["output_file_name"]
+ + "_"
+ + dsplit
+ + "_"
+ + str(i)
+ + ".txt"
+ )
+
+ # save the txt list
+ if "output_lists" not in cfg:
+ cfg["output_lists"] = list()
+ cfg["output_lists"].append(fpath_output)
+
+ with open(fpath_output, "w") as f_out:
+ for j in range(len(imput_groups_dict)):
+ keys = list(imput_groups_dict.keys())
+
+ for fpath in imput_groups_dict[keys[j]][i]:
+ # also count here the actual sizes
+ final_number_of_events[j] += get_number_of_evts(fpath)
+ f_out.write(fpath + "\n")
+
+ # and then print them
+ for i in range(len(imput_groups_dict)):
+ print(keys[i], ":", int(final_number_of_events[i]))
+
+ print("----------------------------------------------")
+
+
+def make_concatenate_and_shuffle_scripts(cfg):
+ """
+ Function that writes qsub .sh files which concatenates all files inside the list files.
+
+ Parameters
+ ----------
+ cfg : dict
+ Dict that contains all configuration options and additional information.
+
+ """
+
+ dirpath = cfg["output_file_folder"]
+
+ if not os.path.exists(
+ dirpath + "/logs"
+ ): # check if /logs folder exists, if not create it.
+ os.makedirs(dirpath + "/logs")
+ if not os.path.exists(
+ dirpath + "/job_scripts"
+ ): # check if /job_scripts folder exists, if not create it.
+ os.makedirs(dirpath + "/job_scripts")
+ if not os.path.exists(
+ dirpath + "/data_split"
+ ): # check if /data_split folder exists, if not create it.
+ os.makedirs(dirpath + "/data_split")
+
+ # not available atm...
+ # chunksize = '' if cfg['chunksize'] is None else ' --chunksize ' + str(cfg['chunksize'])
+ # complib = '' if cfg['complib'] is None else ' --complib ' + str(cfg['complib'])
+ # complevel = '' if cfg['complevel'] is None else ' --complevel ' + str(cfg['complevel'])
+
+ # make qsub .sh file for concatenating
+ for listfile_fpath in cfg["output_lists"]:
+ listfile_fname = os.path.basename(listfile_fpath)
+ listfile_fname_wout_ext = os.path.splitext(listfile_fname)[0]
+ conc_outputfile_fpath = (
+ cfg["output_file_folder"] + "/data_split/" + listfile_fname_wout_ext + ".h5"
+ )
+
+ fpath_bash_script = (
+ dirpath + "/job_scripts/concatenate_h5_" + listfile_fname_wout_ext + ".sh"
+ )
+
+ with open(fpath_bash_script, "w") as f:
+ f.write("#!/usr/bin/env bash\n")
+ f.write("\n")
+ f.write("source " + cfg["venv_path"] + "activate" + "\n")
+ f.write("\n")
+ f.write("# Concatenate the files in the list\n")
+
+ f.write(
+ "concatenate " + listfile_fpath + " --outfile " + conc_outputfile_fpath
+ )
+ # at the moment it is not possible to set the comp opts like this+ chunksize + complib + complevel
+
+ # make qsub .sh file for shuffling
+
+ for listfile_fpath in cfg["output_lists"]:
+ listfile_fname = os.path.basename(listfile_fpath)
+ listfile_fname_wout_ext = os.path.splitext(listfile_fname)[0]
+
+ # This is the input for the shuffle tool!
+ conc_outputfile_fpath = (
+ cfg["output_file_folder"] + "/data_split/" + listfile_fname_wout_ext + ".h5"
+ )
+
+ fpath_bash_script = (
+ dirpath + "/job_scripts/shuffle_h5_" + listfile_fname_wout_ext + ".sh"
+ )
+
+ with open(fpath_bash_script, "w") as f:
+ f.write("#!/usr/bin/env bash\n")
+ f.write("\n")
+ f.write("source " + cfg["venv_path"] + "activate \n")
+ f.write("\n")
+ f.write("# Shuffle the h5 file \n")
+
+ f.write(
+ "h5shuffle2 " + conc_outputfile_fpath + " --max_ram 1000000000 \n"
+ ) # fix to 1GB ram; in lyon using a fraction
+ # is difficult...
+ # time python shuffle/shuffle_h5.py'
+ # + delete_flag_shuffle_tool
+ # + chunksize + complib + complevel
+
+ if cfg["shuffle_delete"]:
+ f.write("\n")
+ f.write("rm " + conc_outputfile_fpath + "\n")
+
+
+def main():
+ """
+ Main function to make the data split.
+ """
+
+ # load the config
+ parser = get_parser()
+ parsed_args = parser.parse_args()
+
+ config_file = parsed_args.config
+
+ # decode config
+ cfg = toml.load(config_file)
+ cfg["toml_filename"] = config_file
+
+ # set some defaults/Nones - at the moment setting of the com opts is not available!
+ # if 'chunksize' not in cfg: cfg['chunksize'] = None
+ # if 'complib' not in cfg: cfg['complib'] = None
+ # if 'complevel' not in cfg: cfg['complevel'] = None
+
+ # read out all the input groups
+ ip_group_keys = get_all_ip_group_keys(cfg)
+
+ # and now loop over input groups extracting info
+ n_evts_total = 0
+ for key in ip_group_keys:
+ print("Collecting information from input group " + key)
+ cfg[key]["fpaths"] = get_h5_filepaths(cfg[key]["dir"])
+ cfg[key]["n_files"] = len(cfg[key]["fpaths"])
+ (
+ cfg[key]["n_evts"],
+ cfg[key]["n_evts_per_file_mean"],
+ cfg[key]["run_ids"],
+ ) = get_number_of_evts_and_run_ids(cfg[key]["fpaths"], dataset_key="y")
+
+ n_evts_total += cfg[key]["n_evts"]
+
+ cfg["n_evts_total"] = n_evts_total
+ # print the extracted statistics
+ print_input_statistics(cfg, ip_group_keys)
+
+ if cfg["print_only"] is True:
+ from sys import exit
+
+ exit()
+
+ for key in ip_group_keys:
+ add_fpaths_for_data_split_to_cfg(cfg, key)
+
+ # create the list files
+ make_dsplit_list_files(cfg)
+
+ # create bash scripts that can be submitted to do the concatenation and shuffle
+ if cfg["make_qsub_bash_files"] is True:
+ make_concatenate_and_shuffle_scripts(cfg)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/orcasong_contrib/__init__.py b/orcasong_contrib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/orcasong_contrib/data_tools/__init__.py b/orcasong_contrib/data_tools/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/orcasong_contrib/data_tools/concatenate/__init__.py b/orcasong_contrib/data_tools/concatenate/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/orcasong_contrib/data_tools/concatenate/concatenate_h5.py b/orcasong_contrib/data_tools/concatenate/concatenate_h5.py
deleted file mode 100644
index bbb0f126395619aa9e5226c34a93feebd1877db3..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/concatenate/concatenate_h5.py
+++ /dev/null
@@ -1,319 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""Concatenates .h5 files. Works only for files where each dataset has the same number of rows."""
-
-import h5py
-import numpy as np
-import math
-from argparse import ArgumentParser, RawTextHelpFormatter
-import sys
-# from memory_profiler import profile # for memory profiling, call with @profile; myfunc()
-
-__author__ = 'Michael Moser'
-__license__ = 'AGPL'
-__version__ = '1.0'
-__email__ = 'michael.m.moser@fau.de'
-__status__ = 'Production'
-
-
-def parse_input():
- """
- Parses the user input in order to return the most important information:
-
- 1) list of files that should be concatenated
- 2) the filepath of the output .h5 file
- 3) use custom chunksize or not.
-
- Returns
- -------
- file_list : list
- List that contains all filepaths of the input files.
- output_filepath : str
- String that specifies the filepath (path+name) of the output .h5 file.
- chunksize : None/int
- Specifies the chunksize for axis_0 in the concatenated output files.
- If None, the chunksize is read from the first input file.
- Else, a custom chunksize will be used.
- complib : None/str
- Specifies the compression library that should be used for saving the concatenated output files.
- If None, the compression library is read from the first input file.
- Else, a custom compression library will be used.
- Currently available: 'gzip', or 'lzf'.
- complevel : None/int
- Specifies the compression level that should be used for saving the concatenated output files.
- A compression level is only available for gzip compression, not lzf!
- If None, the compression level is read from the first input file.
- Else, a custom compression level will be used.
-
- """
- parser = ArgumentParser(description='E.g. < python concatenate_h5.py file_1 file_2 /path/to/output.h5 > or '
- '< python concatenate_h5.py --list filepaths.txt /path/to/output.h5 >.\n'
- 'Concatenates arrays stored in .h5 files for either multiple direct .h5 inputs or a .txt file of .h5 files (--list option).\n'
- 'Outputs a new .h5 file with the concatenated arrays. This output is chunked!\n'
- 'Careful: The folders of one file need to have the same number of rows (axis_0)!\n'
- 'Make a .txt file with < find /path/to/files -name "file_x-*.h5" | sort --version-sort > listname.list >\n'
- 'Chunksize: By default, the chunksize is set to the chunksize of the first inputfile!',
- formatter_class=RawTextHelpFormatter)
-
- parser.add_argument('files', metavar='file', type=str, nargs='*', help = 'a file that should be concatenated, minimum of two.')
- parser.add_argument('output_filepath', metavar='output_filepath', type=str, nargs=1, help='filepath and name of the output .h5 file')
- parser.add_argument('-l', '--list', dest='list_file', type=str,
- help='filepath of a .list file that contains all .h5 files that should be concatenated')
- parser.add_argument('--chunksize', dest='chunksize', type=int,
- help='Specify a chunksize value in order to use chunked storage for the concatenated .h5 file.'
- ' Otherwise, it will be read from the first input file..')
- parser.add_argument('--complib', dest='complib', type=str,
- help='Specify a filter that should be used for compression. Either "gzip" or "lzf". '
- 'Otherwise, the filter will be read from the first input file.')
- parser.add_argument('--complevel', dest='complevel', type=int,
- help='Specify a compression filter strength that should be used for the compression. '
- 'Otherwise, the filter will be read from the first input file. '
- 'Can range from 0 to 9. Has no effect on "lzf" compression.')
-
- if len(sys.argv) == 1:
- parser.print_help()
- sys.exit(1)
-
- args = parser.parse_args()
-
- if args.list_file:
- file_list = []
- for line in open(args.list_file):
- filepath = line.rstrip('\n')
- if filepath != "":
- file_list.append(filepath)
- else:
- file_list = []
- for filepath in args.files:
- file_list.append(filepath)
-
- output_filepath = args.output_filepath[0]
-
- chunksize = None
- if args.chunksize:
- chunksize = args.chunksize
- print('You chose chunksize = ' + str(chunksize))
-
- complib = None
- if args.complib:
- complib = args.complib
- print('You chose complib = ' + complib)
-
- complevel = None
- if args.complevel:
- complevel = args.complevel
- print('You chose complevel = ' + str(complevel))
-
- return file_list, output_filepath, chunksize, complib, complevel
-
-
-def get_cum_number_of_rows(file_list):
- """
- Returns the cumulative number of rows (axis_0) in a list based on the
- specified input .h5 files (i.e. [0,100,200,300,...] if each file has
- 100 rows).
-
- Parameters
- ----------
- file_list : list
- List that contains all filepaths of the input files.
-
- Returns
- -------
- cum_number_of_rows_list : list
- List that contains the cumulative number of rows
- (i.e. [0,100,200,300,...] if each file has 100 rows).
-
- """
- total_number_of_rows = 0
- cum_number_of_rows_list = [0]
- number_of_rows_list = [] # used for approximating the chunksize
-
- # Get total number of rows for the files in the list, faster than resizing
- # the dataset in each iteration of the file loop in concatenate_h5_files()
-
- for file_name in file_list:
- with h5py.File(file_name, 'r') as f:
- # get number of rows from the first folder of the file
- # -> each folder needs to have the same number of rows
- f_keys = list(f.keys())
- f_keys_stripped = strip_keys(f_keys)
-
- total_number_of_rows += f[f_keys_stripped[0]].shape[0]
- cum_number_of_rows_list.append(total_number_of_rows)
- number_of_rows_list.append(f[f_keys_stripped[0]].shape[0])
-
- return cum_number_of_rows_list
-
-
-def strip_keys(f_keys):
- """
- Remove pytables folders starting with '_i_', because the shape
- of its first axis does not correspond to the number of events
- in the file. All other folders normally have an axis_0 shape
- that is equal to the number of events in the file.
- Also remove bin_stats.
- """
- f_keys_stripped = []
- for x in f_keys:
- if is_folder_ignored(x):
- continue
- f_keys_stripped.append(x)
- return f_keys_stripped
-
-
-def is_folder_ignored(folder_name):
- """
- Defines pytable folders which should be ignored during concat.
- """
- if '_i_' in folder_name or "bin_stats" in folder_name:
- return True
- else:
- return False
-
-
-def get_f_compression_and_chunking(filepath):
- """
- Function that gets the used compression library, the compression level (if applicable)
- and the chunksize of axis_0 of the first dataset of the file.
-
- Parameters
- ----------
- filepath : str
- Filepath of a .hdf5 file.
-
- Returns
- -------
- compression : str
- The compression library that has been identified in the input file. E.g. 'gzip', or 'lzf'.
- complevel : int
- The compression level that has been identified in the input file.
- chunksize : None/int
- The chunksize of axis_0 that has been indentified in the input file.
-
- """
- f = h5py.File(filepath, 'r')
-
- # remove any keys to pytables folders that may be in the file
- f_keys = list(f.keys())
- f_keys_stripped = strip_keys(f_keys)
-
- compression = f[f_keys_stripped[0]].compression # compression filter
- compression_opts = f[f_keys_stripped[0]].compression_opts # filter strength
- chunksize = f[f_keys_stripped[0]].chunks[0] # chunksize along axis_0 of the dataset
-
- return compression, compression_opts, chunksize
-
-
-def concatenate_h5_files(output_filepath, file_list,
- chunksize=None, complib=None, complevel=None):
- """
- Function that concatenates hdf5 files based on an output_filepath and a file_list of input files.
-
- If the files contain group_info and x_indices folders (if the input files are coming from km3pipe output),
- the group-id / the index of the x_indices is fixed in order to not get duplicates of group-ids / x-indices.
-
- Parameters
- ----------
- output_filepath : str
- String that specifies the filepath (path+name) of the output .h5 file.
- file_list : list
- List that contains all filepaths of the input files.
- chunksize : None/int
- Specifies the chunksize for axis_0 in the concatenated output files.
- If None, the chunksize is read from the first input file.
- Else, a custom chunksize will be used.
- complib : None/str
- Specifies the compression library that should be used for saving the concatenated output files.
- If None, the compression library is read from the first input file.
- Else, a custom compression library will be used.
- Currently available: 'gzip', or 'lzf'.
- complevel : None/int
- Specifies the compression level that should be used for saving the concatenated output files.
- A compression level is only available for gzip compression, not lzf!
- If None, the compression level is read from the first input file.
- Else, a custom compression level will be used.
-
- """
- cum_rows_list = get_cum_number_of_rows(file_list)
- complib_f, complevel_f, chunksize_f = get_f_compression_and_chunking(file_list[0])
-
- chunksize = chunksize_f if chunksize is None else chunksize
- complib = complib_f if complib is None else complib
- complevel = complevel_f if complevel is None else complevel
-
- if complib == 'lzf':
- complevel = None
-
- file_output = h5py.File(output_filepath, 'w')
-
- for n, input_file_name in enumerate(file_list):
- print('Processing file ' + file_list[n])
- input_file = h5py.File(input_file_name, 'r')
-
- # create metadata
- if 'format_version' in list(input_file.attrs.keys()) and n == 0:
- file_output.attrs['format_version'] = input_file.attrs['format_version']
-
- for folder_name in input_file:
- if is_folder_ignored(folder_name):
- # we ignore datasets that have been created by pytables, don't need them anymore
- continue
-
- if n > 0 and folder_name in ['event_info', 'group_info', 'x_indices', 'y']:
- folder_data = input_file[folder_name][()]
- # we need to add the current number of the group_id / index in the file_output
- # to the group_ids / indices of the file that is to be appended
- column_name = 'group_id' if folder_name in ['event_info', 'group_info', 'y'] else 'index'
- # add 1 because the group_ids / indices start with 0
- folder_data[column_name] += np.amax(file_output[folder_name][column_name]) + 1
-
- else:
- folder_data = input_file[folder_name]
-
- print('Shape and dtype of dataset ' + folder_name + ': ' + str(folder_data.shape) + ' ; ' + str(folder_data.dtype))
-
- if n == 0:
- # first file; create the dummy dataset with no max shape
- maxshape = (None,) + folder_data.shape[1:] # change shape of axis zero to None
- chunks = (chunksize,) + folder_data.shape[1:]
-
- output_dataset = file_output.create_dataset(folder_name, data=folder_data, maxshape=maxshape, chunks=chunks,
- compression=complib, compression_opts=complevel)
-
- output_dataset.resize(cum_rows_list[-1], axis=0)
-
- else:
- file_output[folder_name][cum_rows_list[n]:cum_rows_list[n+1]] = folder_data
-
- file_output.flush()
-
- print('Output information:')
- print('-------------------')
- print('The output file contains the following datasets:')
- for folder_name in file_output:
- print('Dataset ' + folder_name + ' with the following shape, dtype and chunks (first argument'
- ' is the chunksize in axis_0): \n' + str(file_output[folder_name].shape) + ' ; ' +
- str(file_output[folder_name].dtype) + ' ; ' + str(file_output[folder_name].chunks))
-
- file_list_ascii = [n.encode("ascii", "ignore") for n in file_list]
- file_output.create_dataset("used_files", data=file_list_ascii)
-
- file_output.close()
-
-
-def main():
- """
- Main code. Concatenates .h5 files with multiple datasets, where each dataset in one file needs to have the same number of rows (axis_0).
-
- Gets user input with aid of the parse_input() function. By default, the chunksize for the output .h5 file is automatically computed.
- based on the average number of rows per file, in order to eliminate padding (wastes disk space).
- For faster I/O, the chunksize should be set by the user depending on the use case.
- In deep learning applications for example, the chunksize should be equal to the batch size that is used later on for reading the data.
- """
- file_list, output_filepath, chunksize, complib, complevel = parse_input()
- concatenate_h5_files(output_filepath, file_list, chunksize, complib, complevel)
-
-
-if __name__ == '__main__':
- main()
diff --git a/orcasong_contrib/data_tools/make_data_split/__init__.py b/orcasong_contrib/data_tools/make_data_split/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-c_tight-0_100b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-c_tight-0_100b.toml
deleted file mode 100644
index edf1529caaa38b8d1528dcdbc4332fd9b1ac0422..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-c_tight-0_100b.toml
+++ /dev/null
@@ -1,141 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 29
-n_files_validate = 13
-n_files_rest = 50
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/data_splits/xyzc"
-output_file_name = "xyzc_tight_0_100b_bg_classifier_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = false
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = false
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-[mupage] # 1 to 20000
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/mupage/xyzc"
-run_ids_train = [15618, 20000]
-run_ids_validate = [13741, 15617]
-run_ids_rest = [1, 13740]
-
-
-[random_noise] # 1 to 1500
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/random_noise/xyzc"
-run_ids_train = [576, 1500]
-run_ids_validate = [186, 575]
-run_ids_rest = [1, 185]
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/muon-CC/3-100GeV/xyzc"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/muon-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-CC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-NC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-NC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/tau-CC/3-100GeV/xyzc"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-t_tight-0_100b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-t_tight-0_100b.toml
deleted file mode 100644
index d3d3648b5fc418c655bee5b9abfd0bf858712223..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_bg_classifier_xyz-t_tight-0_100b.toml
+++ /dev/null
@@ -1,141 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 29
-n_files_validate = 13
-n_files_rest = 50
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/data_splits/xyzt"
-output_file_name = "xyzt_tight_0_100b_bg_classifier_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = false
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = false
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-[mupage] # 1 to 20000
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/mupage/xyzt"
-run_ids_train = [15618, 20000]
-run_ids_validate = [13741, 15617]
-run_ids_rest = [1, 13740]
-
-
-[random_noise] # 1 to 1500
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/random_noise/xyzt"
-run_ids_train = [576, 1500]
-run_ids_validate = [186, 575]
-run_ids_rest = [1, 185]
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/muon-CC/3-100GeV/xyzt"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/muon-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-CC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-NC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/elec-NC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_0_100b_t_bg_classifier/tau-CC/3-100GeV/xyzt"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-c_tight-1_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-c_tight-1_60b.toml
deleted file mode 100644
index 7a2d12e1dd76b397d318388105a28581fd826f55..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-c_tight-1_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/data_splits/xyzc"
-output_file_name = "xyzc_tight_1_60b_regression_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = false
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/muon-CC/3-100GeV/xyzc"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/muon-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-CC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-NC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-NC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/tau-CC/3-100GeV/xyzc"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-t_tight-1_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-t_tight-1_60b.toml
deleted file mode 100644
index 574343064bb1486bc093fa2c93103201f2ecb241..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_regression_xyz-t_tight-1_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/data_splits/xyzt"
-output_file_name = "xyzt_tight_1_60b_regression_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = false
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/muon-CC/3-100GeV/xyzt"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/muon-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-CC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-NC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/elec-NC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_regression_classifier/tau-CC/3-100GeV/xyzt"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-1_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-1_60b.toml
deleted file mode 100644
index d0d2f021e4abe7f8c9c777232ffa3d2f472c6005..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-1_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/data_splits/xyzc"
-output_file_name = "xyzc_tight_1_60b_ts_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = true
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/muon-CC/3-100GeV/xyzc"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/muon-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-CC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-NC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-NC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/tau-CC/3-100GeV/xyzc"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-2_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-2_60b.toml
deleted file mode 100644
index 9ba8564c518051f9585d4fc0ce63fff38e44585a..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-c_tight-2_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/data_splits/xyzc"
-output_file_name = "xyzc_tight_2_60b_ts_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = true
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/muon-CC/3-100GeV/xyzc"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/muon-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-CC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-CC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-NC/3-100GeV/xyzc"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-NC/1-5GeV/xyzc"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/tau-CC/3-100GeV/xyzc"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-1_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-1_60b.toml
deleted file mode 100644
index b913e8451dd61ce50af6bcdf850d6549336decad..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-1_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/data_splits/xyzt"
-output_file_name = "xyzt_tight_1_60b_ts_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = true
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/muon-CC/3-100GeV/xyzt"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/muon-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-CC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-NC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/elec-NC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_1_60b_ts_classifier/tau-CC/3-100GeV/xyzt"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-2_60b.toml b/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-2_60b.toml
deleted file mode 100644
index 5fcc6daab6c1a2beb7a3a47aa265807dc8a3193f..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/configs/config_ts_xyz-t_tight-2_60b.toml
+++ /dev/null
@@ -1,129 +0,0 @@
-# Example configuration file for make_data_split.py
-
-# --- Documentation for every config parameter that is available --- #
-#
-# Main Parameters
-# ----------
-# n_files_train : int
-# Into how many files the training dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_validate : int
-# Into how many files the validation dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# n_files_rest : int
-# Into how many files the "rest" dataset should be split.
-# If you don't want to have this dataset, comment out the line or delete it!
-# output_file_folder : str
-# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
-# output_file_name : str
-# String, that specifies the prefix of the filename of the output .list files.
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-# print_only : bool
-# If only informationa about the input_groups should be printed, and no .list files should be made.
-#
-# Job submission Parameters
-# -------------------------
-# make_qsub_bash_files : bool
-# If bash files should be made, that can be submitted to a cluster, in order to actually concatenate
-# the files in the .list files.
-# submit_jobs : bool
-# Additionally to make_qsub_bash_files, submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC.
-# venv_path : str
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# data_tools_folder : str
-# Dirpath, where the concatenate.py tool is located.
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-# chunksize : int
-# Chunksize parameter, that is used when calling concatenate.py
-# complib : str
-# Complib parameter, that is used when calling concatenate.py
-# complevel : int
-# Complevel parameter, that is used when calling concatenate.py
-# shuffle_delete : bool
-# Option for the shuffle_h5 tool, specifies if the input file that will be shuffled should be
-# deleted after the shuffling is finished.
-#
-# Input Group Parameters
-# ----------------------
-# dir : str
-# Path of the directory, where the files for this input group are located.
-# run_ids_train/run_ids_validate/run_ids_rest : array
-# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
-# dataset of this input group.
-# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
-# to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
-#
-# --- Documentation for every config parameter that is available --- #
-
-# --- Main options ---#
-
-n_files_train = 10
-n_files_validate = 5
-n_files_rest = 1
-output_file_folder = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/data_splits/xyzt"
-output_file_name = "xyzt_tight_2_60b_ts_dataset"
-print_only = false # only print information of your input_groups, don't make any .list files
-
-# --- Main options ---#
-
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-make_qsub_bash_files = true
-submit_jobs = true
-venv_path = "/home/hpc/capn/mppi033h/.virtualenv/python_3_env"
-data_tools_folder = "/home/woody/capn/mppi033h/Code/OrcaSong/orcasong_contrib/data_tools"
-chunksize = 32
-complib = "gzip"
-complevel = 1
-shuffle_delete = true
-
-# --- Options, for submitting jobs to concatenate the .list files. --- #
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
-
-
-[muon_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/muon-CC/3-100GeV/xyzt"
-run_ids_train = [721, 2400]
-run_ids_validate = [1, 720]
-
-
-[muon_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/muon-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_cc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-CC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_cc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-CC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[elec_nc_3_100] # 1 to 2400
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-NC/3-100GeV/xyzt"
-run_ids_train = [361, 1200]
-run_ids_validate = [1, 360]
-
-
-[elec_nc_1_5] # 1 to 600
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/elec-NC/1-5GeV/xyzt"
-run_ids_train = [181, 600]
-run_ids_validate = [1, 180]
-
-
-[tau_cc_3_100] # 1 to 1800
-dir = "/home/saturn/capn/mppi033h/Data/input_images/ORCA_2016_115l/tight_2_60b_ts_classifier/tau-CC/3-100GeV/xyzt"
-run_ids_rest = [1, 1800]
-
-# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/orcasong_contrib/data_tools/make_data_split/example_config.toml b/orcasong_contrib/data_tools/make_data_split/example_config.toml
deleted file mode 100644
index 6d2ca026c4c2d23eee490aca58db8ba0b7c2b762..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/example_config.toml
+++ /dev/null
@@ -1,134 +0,0 @@
-# --- Example configuration file for make_data_split.py --- #
-# Documentation for every config parameter that is available.
-# Feel free to make a copy and keep only the lines you need!
-# If you don't want to have a parameter, comment out the line or delete it!
-
-# --- Main Parameters --- #
-
-# ----------------------------------------------------------------------------- #
-# Into how many files the training dataset should be split. Type: int
-# This option is needed if one of your input_groups has a run_id_train range.
-
-n_files_train = 5
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Into how many files the validation dataset should be split. Type: int
-# This option is needed if one of your input_groups has a run_id_validate range.
-
-n_files_validate = 1
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Into how many files the "rest" dataset should be split. Type: int
-# This option is needed if one of your input_groups has a run_id_rest range.
-
-n_files_rest = 1
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Path to the folder, where all the output .list files
-# (and the bash job scripts) should be stored. Type: str
-
-output_file_folder = "/path/to/output/folder"
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Prefix of the filename of the output .list files. Type: str
-# E.g. if = "xyzc_tight_0":
-# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
-
-output_file_name = "output_file_name"
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# true = No .list files are made, only prints information about the input_groups.
-# Type: bool
-
-print_only = false
-# ----------------------------------------------------------------------------- #
-
-
-# --- Job Submission Parameters --- #
-
-# ----------------------------------------------------------------------------- #
-# true = Makes the cluster submission bash files needed to actually
-# concatenate the files in the .list files. Type: bool
-
-make_qsub_bash_files = true
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# true = Submit the bash job scripts to the cluster after they have been made.
-# CAREFUL: May only work for Erlangen-CC. Type: bool
-
-submit_jobs = false
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Path to a virtualenv, e.g. "/home/hpc/capn/mppi033h/.virtualenv/python_3_env/"
-# Type: str
-
-venv_path = "/path/to/your/venv"
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Dirpath, where the concatenate.py tool is located. Type: str
-# E.g. "/home/woody/capn/mppi033h/Code/OrcaNet/orcanet_contrib/data_tools"
-
-data_tools_folder = "path/to/OrcaNet/orcanet_contrib/data_tools"
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# True = the input file that will be deleted after the shuffling is finished.
-# Option for the shuffle_h5 tool. Type: bool
-shuffle_delete = false
-
-# ----------------------------------------------------------------------------- #
-
-# ----------------------------------------------------------------------------- #
-# Concatenate.py & shuffle.py Parameters
-# If they are commented, it will be set None on concatenate.py,
-# and the script will use the chunksize/complib/complevel that are already
-# used in the input files for these scripts.
-
-#chunksize = 32 # Type: int
-#complib = "gzip" # Type: str
-#complevel = 1 # Type: int
-
-# ----------------------------------------------------------------------------- #
-
-
-# --- Input Group Parameters: Datafiles to be concatenated --- #
-
-# ----------------------------------------------------------------------------- #
-
-[input_group_1] # Name of the group, don't make whitespaces!
-
-# Type: str
-# Path of the directory where the files for this input group are located.
-dir = "/path/to/the/folder/of/the/data/for/this/input_1/group"
-
-# Arrays with the range of the run_ids that should be used for the
-# training, validation and rest datasets of this input group.
-# E.g. if [1,5] = Files from this input group with run_ids from 1 to 5
-# (including 1 and 5!) will go to the training/validation/rest dataset.
-# If you don't want to use a specific dataset for this input group,
-# you can comment out the corresponding line or delete it!
-run_ids_train = [1, 200]
-run_ids_validate = [201, 1200]
-#run_ids_rest = [1201, 1300]
-
-# ----------------------------------------------------------------------------- #
-# You can have more than 1 input group!
-
-[input_group_2] # Name of the group, don't make whitespaces!
-
-# Type: str
-# Path of the directory where the files for this input group are located.
-dir = "/path/to/the/folder/of/the/data/for/this/input_2/group"
-
-run_ids_train = [1, 1000]
-run_ids_validate = [1001, 1188]
-
-# ----------------------------------------------------------------------------- #
diff --git a/orcasong_contrib/data_tools/make_data_split/make_data_split.py b/orcasong_contrib/data_tools/make_data_split/make_data_split.py
deleted file mode 100644
index 65e68a01198a6afbe37d25190d00f2f0311cc6aa..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/make_data_split/make_data_split.py
+++ /dev/null
@@ -1,387 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-Utility script that makes .list files for the concatenate_h5.py tool.
-
-Usage:
- make_data_split.py CONFIG
- make_data_split.py (-h | --help)
-
-Arguments:
- CONFIG A .toml file which contains the configuration options.
-
-Options:
- -h --help Show this screen.
-
-"""
-
-__author__ = 'Michael Moser'
-
-import os
-import toml
-import docopt
-import natsort as ns
-import h5py
-import random
-
-def parse_input():
- """
- Parses the config of the .toml file, specified by the user.
-
- Returns
- -------
- cfg : dict
- Dict that contains all configuration options from the input .toml file.
-
- """
-
- args = docopt.docopt(__doc__)
- config_file = args['CONFIG']
-
- cfg = toml.load(config_file)
- cfg['toml_filename'] = config_file
-
- if 'chunksize' not in cfg: cfg['chunksize'] = None
- if 'complib' not in cfg: cfg['complib'] = None
- if 'complevel' not in cfg: cfg['complevel'] = None
- return cfg
-
-
-def get_all_ip_group_keys(cfg):
- """
- Gets the keys of all input groups in the config dict.
-
- The input groups are defined as the dict elements, where the values have the type of a dict.
-
- Parameters
- ----------
- cfg : dict
- Dict that contains all configuration options and additional information.
-
- Returns
- -------
- ip_group_keys : list
- List of the input_group keys.
-
- """
- ip_group_keys = []
- for key in cfg:
- if type(cfg[key]) == dict:
- ip_group_keys.append(key)
-
- return ip_group_keys
-
-
-def get_h5_filepaths(dirpath):
- """
- Returns the filepaths of all .h5 files that are located in a specific directory.
-
- Parameters
- ----------
- dirpath: str
- Path of the directory where the .h5 files are located.
-
- Returns
- -------
- filepaths : list
- List with the full filepaths of all .h5 files in the dirpath folder.
-
- """
- filepaths = []
- for f in os.listdir(dirpath):
- if f.endswith('.h5'):
- filepaths.append(dirpath + '/' + f)
-
- #randomize order
- random.Random(42).shuffle(filepaths)
-
- return filepaths
-
-
-def get_number_of_evts_and_run_ids(list_of_files, dataset_key='y', run_id_col_name='run_id'):
- """
- Gets the number of events and the run_ids for all hdf5 files in the list_of_files.
-
- The number of events is calculated based on the dataset, which is specified with the dataset_key parameter.
-
- Parameters
- ----------
- list_of_files : list
- List which contains filepaths to h5 files.
- dataset_key : str
- String which specifies, which dataset in a h5 file should be used for calculating the number of events.
- run_id_col_name : str
- String, which specifies the column name of the 'run_id' column.
-
- Returns
- -------
- total_number_of_evts : int
- The cumulative (total) number of events.
- mean_number_of_evts_per_file : float
- The mean number of evts per file.
- run_ids : list
- List containing the run_ids of the files in the list_of_files.
-
- """
-
- total_number_of_evts = 0
- run_ids = []
-
- for i, fpath in enumerate(list_of_files):
- f = h5py.File(fpath, 'r')
-
- dset = f[dataset_key]
- n_evts = dset.shape[0]
- total_number_of_evts += n_evts
-
- run_id = f[dataset_key][0][run_id_col_name]
- run_ids.append(run_id)
-
- f.close()
-
- mean_number_of_evts_per_file = total_number_of_evts / len(list_of_files)
-
- return total_number_of_evts, mean_number_of_evts_per_file, run_ids
-
-
-def split(a, n):
- """
- Splits a list into n equal sized (if possible! if not, approximately) chunks.
-
- Parameters
- ----------
- a : list
- A list that should be split.
- n : int
- Number of times the input list should be split.
-
- Returns
- -------
- a_split : list
- The input list a, which has been split into n chunks.
-
- """
- # from https://stackoverflow.com/questions/2130016/splitting-a-list-into-n-parts-of-approximately-equal-length
- k, m = divmod(len(a), n)
- a_split = list((a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n)))
- return a_split
-
-
-def print_input_statistics(cfg, ip_group_keys):
- """
- Prints some useful information for each input_group.
-
- Parameters
- ----------
- cfg : dict
- Dict that contains all configuration options and additional information.
- ip_group_keys : list
- List of the input_group keys.
-
- """
-
- print('----------------------------------------------------------------------')
- print('Printing input statistics for your ' + cfg['toml_filename'] + ' input:')
- print('----------------------------------------------------------------------')
-
- print('Your input .toml file has the following data input groups: ' + str(ip_group_keys))
- print('Total number of events: ' + str(cfg['n_evts_total']))
-
- for key in ip_group_keys:
- print('--------------------------------------------------------------------')
- print('Info for group ' + key + ':')
- print('Directory: ' + cfg[key]['dir'])
- print('Total number of files: ' + str(cfg[key]['n_files']))
- print('Total number of events: ' + str(cfg[key]['n_evts']))
- print('Mean number of events per file: ' + str(round(cfg[key]['n_evts_per_file_mean'], 3)))
- print('--------------------------------------------------------------------')
-
-
-def add_fpaths_for_data_split_to_cfg(cfg, key):
- """
- Adds all the filepaths for the output files into a list, and puts them into the cfg['output_dsplit'][key] location
- for all dsplits (train, validate, rest).
-
- Parameters
- ----------
- cfg : dict
- Dict that contains all configuration options and additional information.
- key : str
- The key of an input_group.
-
- """
-
- fpath_lists = {'train': [], 'validate': [], 'rest': []}
- for i, fpath in enumerate(cfg[key]['fpaths']):
-
- run_id = cfg[key]['run_ids'][i]
-
- for dsplit in ['train', 'validate', 'rest']:
- if 'run_ids_' + dsplit in cfg[key]:
- if cfg[key]['run_ids_' + dsplit][0] <= run_id <= cfg[key]['run_ids_' + dsplit][1]:
- fpath_lists[dsplit].append(fpath)
-
- for dsplit in ['train', 'validate', 'rest']:
- if len(fpath_lists[dsplit]) == 0:
- continue
-
- n_files_dsplit = cfg['n_files_' + dsplit]
- fpath_lists[dsplit] = split(fpath_lists[dsplit], n_files_dsplit)
- if 'output_' + dsplit not in cfg:
- cfg['output_' + dsplit] = dict()
- cfg['output_' + dsplit][key] = fpath_lists[dsplit]
-
-
-def make_dsplit_list_files(cfg):
- """
- Writes .list files of the datasplits to the disk, with the information in the cfg['output_dsplit'] dict.
-
- Parameters
- ----------
- cfg : dict
- Dict that contains all configuration options and additional information.
-
- """
- # check if //conc_list_files folder exists, if not create it.
- if not os.path.exists(cfg['output_file_folder'] + '/conc_list_files'):
- os.makedirs(cfg['output_file_folder'] + '/conc_list_files')
-
- for dsplit in ['train', 'validate', 'rest']:
-
- if 'output_' + dsplit not in cfg:
- continue
-
- first_key = list(cfg['output_' + dsplit].keys())[0]
- n_output_files = len(cfg['output_' + dsplit][first_key])
-
- for i in range(n_output_files):
- fpath_output = cfg['output_file_folder'] + '/conc_list_files/' + cfg['output_file_name'] + '_' + dsplit + '_' + str(i) + '.list'
-
- # for later usage
- if 'output_lists' not in cfg:
- cfg['output_lists'] = list()
- cfg['output_lists'].append(fpath_output)
-
- with open(fpath_output, 'w') as f_out:
- for group_key in cfg['output_' + dsplit]:
- for fpath in cfg['output_' + dsplit][group_key][i]:
- f_out.write(fpath + '\n')
-
-
-def make_concatenate_and_shuffle_list_files(cfg):
- """
- Function that writes qsub .sh files which concatenates all files inside the .list files.
-
- Parameters
- ----------
- cfg : dict
- Dict that contains all configuration options and additional information.
-
- """
- # TODO include options for multicore
-
- dirpath = cfg['output_file_folder']
-
- if not os.path.exists(dirpath + '/logs'): # check if /logs folder exists, if not create it.
- os.makedirs(dirpath + '/logs')
- if not os.path.exists(dirpath + '/job_scripts'): # check if /job_scripts folder exists, if not create it.
- os.makedirs(dirpath + '/job_scripts')
- if not os.path.exists(dirpath + '/data_split'): # check if /data_split folder exists, if not create it.
- os.makedirs(dirpath + '/data_split')
-
- chunksize = '' if cfg['chunksize'] is None else ' --chunksize ' + str(cfg['chunksize'])
- complib = '' if cfg['complib'] is None else ' --complib ' + str(cfg['complib'])
- complevel = '' if cfg['complevel'] is None else ' --complevel ' + str(cfg['complevel'])
-
- # make qsub .sh file for concatenating
- for listfile_fpath in cfg['output_lists']:
- listfile_fname = os.path.basename(listfile_fpath)
- listfile_fname_wout_ext = os.path.splitext(listfile_fname)[0]
- conc_outputfile_fpath = cfg['output_file_folder'] + '/data_split/' + listfile_fname_wout_ext + '.h5'
-
- fpath_bash_script = dirpath + '/job_scripts/concatenate_h5_' + listfile_fname_wout_ext + '.sh'
-
- with open(fpath_bash_script, 'w') as f:
- f.write('#!/usr/bin/env bash\n')
- f.write('#\n')
- f.write('#PBS -o ' + cfg['output_file_folder'] + '/logs/concatenate_h5_' + listfile_fname_wout_ext + '.out'
- ' -e ' + cfg['output_file_folder'] + '/logs/concatenate_h5_' + listfile_fname_wout_ext + '.err\n')
- f.write('\n')
- f.write('CodeFolder="' + cfg['data_tools_folder'] + '"\n')
- f.write('cd ${CodeFolder}\n')
- f.write('source activate ' + cfg['venv_path'] + '\n')
- f.write('\n')
- f.write('# Concatenate the files in the list\n')
-
- f.write('time python concatenate/concatenate_h5.py'
- + chunksize + complib + complevel
- + ' -l ' + listfile_fpath + ' ' + conc_outputfile_fpath)
-
- if cfg['submit_jobs'] is True:
- os.system('qsub -l nodes=1:ppn=4,walltime=23:59:00 ' + fpath_bash_script)
-
- # make qsub .sh file for shuffling
- delete_flag_shuffle_tool = ' --delete' if cfg['shuffle_delete'] is True else ''
- for listfile_fpath in cfg['output_lists']:
- listfile_fname = os.path.basename(listfile_fpath)
- listfile_fname_wout_ext = os.path.splitext(listfile_fname)[0]
-
- # This is the input for the shuffle tool!
- conc_outputfile_fpath = cfg['output_file_folder'] + '/data_split/' + listfile_fname_wout_ext + '.h5'
-
- fpath_bash_script = dirpath + '/job_scripts/shuffle_h5_' + listfile_fname_wout_ext + '.sh'
-
- with open(fpath_bash_script, 'w') as f:
- f.write('#!/usr/bin/env bash\n')
- f.write('#\n')
- f.write('#PBS -o ' + cfg['output_file_folder'] + '/logs/shuffle_h5_' + listfile_fname_wout_ext + '.out'
- ' -e ' + cfg['output_file_folder'] + '/logs/shuffle_h5_' + listfile_fname_wout_ext + '.err\n')
- f.write('\n')
- f.write('CodeFolder="' + cfg['data_tools_folder'] + '"\n')
- f.write('cd ${CodeFolder}\n')
- f.write('source activate ' + cfg['venv_path'] + '\n')
- f.write('\n')
- f.write('# Shuffle the h5 file \n')
-
- f.write('time python shuffle/shuffle_h5.py'
- + delete_flag_shuffle_tool
- + chunksize + complib + complevel
- + ' ' + conc_outputfile_fpath)
-
-
-def main():
- """
- Main function to make the data split.
- """
-
- cfg = parse_input()
-
- ip_group_keys = get_all_ip_group_keys(cfg)
-
- n_evts_total = 0
- for key in ip_group_keys:
- print('Collecting information from input group ' + key)
- cfg[key]['fpaths'] = get_h5_filepaths(cfg[key]['dir'])
- cfg[key]['n_files'] = len(cfg[key]['fpaths'])
- cfg[key]['n_evts'], cfg[key]['n_evts_per_file_mean'], cfg[key]['run_ids'] = get_number_of_evts_and_run_ids(cfg[key]['fpaths'], dataset_key='y')
-
- n_evts_total += cfg[key]['n_evts']
-
- cfg['n_evts_total'] = n_evts_total
- print_input_statistics(cfg, ip_group_keys)
-
- if cfg['print_only'] is True:
- from sys import exit
- exit()
-
- for key in ip_group_keys:
- add_fpaths_for_data_split_to_cfg(cfg, key)
-
- make_dsplit_list_files(cfg)
-
- if cfg['make_qsub_bash_files'] is True:
- make_concatenate_and_shuffle_list_files(cfg)
-
-
-if __name__ == '__main__':
- main()
diff --git a/orcasong_contrib/data_tools/shuffle/__init__.py b/orcasong_contrib/data_tools/shuffle/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/orcasong_contrib/data_tools/shuffle/shuffle_h5.py b/orcasong_contrib/data_tools/shuffle/shuffle_h5.py
deleted file mode 100644
index 4e0a93c6bd12a364360c69bbe57389a30432d626..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/shuffle/shuffle_h5.py
+++ /dev/null
@@ -1,320 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-Contains functions to shuffles .h5 files.
-
-Can only be used for files where each dataset has the same number of rows (axis_0).
-A fixed random seed (42) is used for the shuffling!
-
-Currently, two types of .h5 files are supported:
-
-1) Files which can be read by km3pipe (e.g. files produced with OrcaSong).
-2) Plain hdf5 files with a hdf5 folder depth of 1. This method is based on some legacy code.
- Be careful to not run out of memory! Needs the unshuffled .h5 file's disk space + the python overhead as memory.
- If you want to use it, please use the --legacy_mode option.
-"""
-
-import sys
-import os
-from argparse import ArgumentParser, RawTextHelpFormatter
-import numpy as np
-import h5py
-import km3pipe as kp
-import km3modules as km
-from orcasong_contrib.data_tools.concatenate.concatenate_h5 import get_f_compression_and_chunking
-import orcasong.modules as os_modules
-
-# from memory_profiler import profile # for memory profiling, call with @profile; myfunc()
-
-__author__ = 'Michael Moser'
-__license__ = 'AGPL'
-__email__ = 'michael.m.moser@fau.de'
-
-
-def parse_input():
- """
- Parses the user input in order to return the most important information:
-
- 1) list of files that should be shuffled
- 2) if the unshuffled file should be deleted
- 3) if the user wants to use a custom chunksize, or if the chunksize should be read from the input file.
- 4) if the user wants to use a custom complib, or if the complib should be read from the input file.
- 5) if the user wants to use a custom complevel, or if the complevel should be read from the input file.
-
- Returns
- -------
- input_files_list : list
- List that contains all filepaths of the input files that should be shuffled.
- delete : bool
- Boolean flag that specifies, if the unshuffled input files should be deleted after the shuffling.
- chunksize : None/int
- Specifies the chunksize for axis_0 in the shuffled output files.
- If None, the chunksize is read from the input files.
- Else, a custom chunksize will be used.
- complib : None/str
- Specifies the compression library that should be used for saving the shuffled output files.
- If None, the compression library is read from the input files.
- Else, a custom compression library will be used.
- Currently available: 'gzip', or 'lzf'.
- complevel : None/int
- Specifies the compression level that should be used for saving the shuffled output files.
- A compression level is only available for gzip compression, not lzf!
- If None, the compression level is read from the input files.
- Else, a custom compression level will be used.
- legacy_mode : bool
- Boolean flag that specifies, if the legacy shuffle mode should be used instead of the standard one.
- A more detailed description of this mode can be found in the summary at the top of this python file.
-
- """
- parser = ArgumentParser(description='E.g. < python shuffle_h5.py filepath_1 [filepath_2] [...] > \n'
- 'Shuffles .h5 files. Requires that each dataset of the files has the same number of rows (axis_0). \n'
- 'Outputs a new, shuffled .h5 file with the suffix < _shuffled >.',
- formatter_class=RawTextHelpFormatter)
-
- parser.add_argument('files', metavar='file', type=str, nargs='+', help='a .h5 file that should be shuffled, can be more than one argument.')
- parser.add_argument('-d', '--delete', action='store_true',
- help='deletes the original input file after the shuffled .h5 is created.')
- parser.add_argument('--chunksize', dest='chunksize', type=int,
- help='Specify a chunksize value in order to use chunked storage for the shuffled .h5 file. \n'
- ' Otherwise, it will be read from the input file..')
- parser.add_argument('--complib', dest='complib', type=str,
- help='Specify a filter that should be used for compression. Either "gzip" or "lzf". \n'
- 'Otherwise, the filter will be read from the input file.')
- parser.add_argument('--complevel', dest='complevel', type=int,
- help='Specify a compression filter strength that should be used for the compression. \n'
- 'Otherwise, the filter will be read from the input file. \n'
- 'Can range from 0 to 9. Has no effect on "lzf" compression.')
- parser.add_argument('--legacy_mode', dest='legacy_mode', action='store_true',
- help='If you want to use the legacy mode, as described in the summary at the top of this python file.')
-
- parser.set_defaults(legacy_mode=False)
-
- if len(sys.argv) == 1:
- parser.print_help()
- sys.exit(1)
-
- args = parser.parse_args()
-
- input_files_list = []
- for filepath in args.files:
- input_files_list.append(filepath)
-
- delete = False
- if args.delete:
- delete = True
- print('You chose delete = True')
-
- chunksize = None
- if args.chunksize:
- chunksize = args.chunksize
- print('You chose chunksize = ' + str(chunksize))
-
- complib = None
- if args.complib:
- complib = args.complib
- print('You chose complib = ' + complib)
-
- complevel = None
- if args.complevel:
- complevel = args.complevel
- print('You chose complevel = ' + str(complevel))
-
- legacy_mode = args.legacy_mode
-
- return input_files_list, delete, chunksize, complib, complevel, legacy_mode
-
-
-def get_filepath_output(filepath_input, shuffle, event_skipper):
- """
- Get the filename of the shuffled / rebalanced output file as a str.
-
- """
- filepath_input_without_ext = os.path.splitext(filepath_input)[0]
- fname_adtn = ''
- if shuffle:
- fname_adtn += '_shuffled'
- if event_skipper is not None:
- fname_adtn += '_reb'
- filepath_output = filepath_input_without_ext + fname_adtn + ".h5"
- return filepath_output
-
-
-def shuffle_h5(filepath_input, tool=False, seed=42, delete=False, chunksize=None,
- complib=None, complevel=None, legacy_mode=False, shuffle=True,
- event_skipper=None, filepath_output=None):
- """
- Shuffles a .h5 file where each dataset needs to have the same number of rows (axis_0).
- The shuffled data is saved to a new .h5 file with the suffix < _shuffled.h5 >.
-
- Can also skip certain events if a event_skipper is given.
-
- Parameters
- ----------
- filepath_input : str
- Filepath of the unshuffled input file.
- tool : bool
- Specifies if the function is accessed from the shuffle_h5_tool.
- In this case, the shuffled .h5 file is returned.
- seed : int
- Sets a fixed random seed for the shuffling.
- delete : bool
- Specifies if the old, unshuffled file should be deleted after extracting the data.
- chunksize : None/int
- Specifies the chunksize for axis_0 in the shuffled output files.
- If None, the chunksize is read from the input files.
- Else, a custom chunksize will be used.
- complib : None/str
- Specifies the compression library that should be used for saving the shuffled output files.
- If None, the compression library is read from the input files.
- Else, a custom compression library will be used.
- Currently available: 'gzip', or 'lzf'.
- complevel : None/int
- Specifies the compression level that should be used for saving the shuffled output files.
- A compression level is only available for gzip compression, not lzf!
- If None, the compression level is read from the input files.
- Else, a custom compression level will be used.
- legacy_mode : bool
- Boolean flag that specifies, if the legacy shuffle mode should be used instead of the standard one.
- A more detailed description of this mode can be found in the summary at the top of this python file.
- shuffle : bool
- If false, events will not be shuffled.
- event_skipper : func, optional
- Function that takes the blob as an input, and returns a bool.
- If the bool is true, the blob will be skipped.
- filepath_output : str, optional
- If given, this will be the name of the output file. Otherwise, a name
- is auto generated.
-
- Returns
- -------
- output_file_shuffled : h5py.File
- H5py file instance of the shuffled output file.
-
- """
- if event_skipper is None and not shuffle:
- raise ValueError("Either event_skipper or shuffle has to be set")
-
- complib_f, complevel_f, chunksize_f = get_f_compression_and_chunking(filepath_input)
-
- chunksize = chunksize_f if chunksize is None else chunksize
- complib = complib_f if complib is None else complib
- complevel = complevel_f if complevel is None else complevel
-
- if complib == 'lzf':
- complevel = None
-
- if filepath_output is None:
- filepath_output = get_filepath_output(filepath_input, shuffle,
- event_skipper)
-
- if not legacy_mode:
- # set random km3pipe (=numpy) seed
- print('Setting a Global Random State with the seed < 42 >.')
- km.GlobalRandomState(seed=seed)
-
- # km3pipe uses pytables for saving the shuffled output file, which has the name 'zlib' for the 'gzip' filter
- if complib == 'gzip':
- complib = 'zlib'
-
- pipe = kp.Pipeline(timeit=True) # add timeit=True argument for profiling
- pipe.attach(km.common.StatusBar, every=200)
- pipe.attach(km.common.MemoryObserver, every=200)
- pipe.attach(kp.io.hdf5.HDF5Pump, filename=filepath_input, shuffle=shuffle, reset_index=True)
- if event_skipper is not None:
- pipe.attach(os_modules.EventSkipper, event_skipper=event_skipper)
-
- pipe.attach(kp.io.hdf5.HDF5Sink, filename=filepath_output, complib=complib, complevel=complevel, chunksize=chunksize, flush_frequency=1000)
- pipe.drain()
-
- # copy the used_files dataset to the new file
- copy_used_files(filepath_input, filepath_output)
-
- if delete:
- os.remove(filepath_input)
-
- # delete folders with '_i_' that are created by pytables in the HDF5Sink, we don't need them
- output_file_shuffled = h5py.File(filepath_output, 'r+')
- for folder_name in output_file_shuffled:
- if folder_name.startswith('_i_'):
- del output_file_shuffled[folder_name]
-
- else:
- input_file = h5py.File(filepath_input, 'r')
- folder_data_array_dict = {}
-
- for folder_name in input_file:
- folder_data_array = input_file[folder_name][()] # get whole numpy array into memory
- folder_data_array_dict[folder_name] = folder_data_array # workaround in order to be able to close the input file at the next step
-
- input_file.close()
-
- if delete:
- os.remove(filepath_input)
-
- output_file_shuffled = h5py.File(filepath_output, 'w')
- for n, dataset_key in enumerate(folder_data_array_dict):
-
- dataset = folder_data_array_dict[dataset_key]
-
- if n == 0:
- # get a particular seed for the first dataset such that the shuffling is consistent across the datasets
- r = np.random.RandomState(seed)
- state = r.get_state()
- r.shuffle(dataset)
-
- else:
- r.set_state(state) # recover shuffle seed of the first dataset
- r.shuffle(dataset)
-
- chunks = (chunksize,) + dataset.shape[1:]
- output_file_shuffled.create_dataset(dataset_key, data=dataset, dtype=dataset.dtype, chunks=chunks,
- compression=complib, compression_opts=complevel)
-
- # close file in the case of tool=True
- if tool is False:
- output_file_shuffled.close()
- else:
- return output_file_shuffled
-
-
-def copy_used_files(source_file, target_file):
- """
- Copy the "used_files" dataset from one h5 file to another, if it is present.
-
- """
- with h5py.File(source_file, "r") as src:
- if "used_files" in src:
- print("Copying used_files dataset to new file...")
- used_files = src["used_files"]
-
- with h5py.File(target_file, "a") as trg:
- trg.create_dataset("used_files", data=used_files)
-
-
-def main():
- """
- Frontend for the shuffle_h5 function that can be used in a bash environment.
-
- Shuffles .h5 files where each dataset needs to have the same number of rows (axis_0) for a single file.
- Saves the shuffled data to a new .h5 file.
- """
- input_files_list, delete, chunksize, complib, complevel, legacy_mode = parse_input()
-
- for filepath_input in input_files_list:
- print('Shuffling file ' + filepath_input)
- output_file_shuffled = shuffle_h5(filepath_input, tool=True, seed=42, delete=delete, chunksize=chunksize,
- complib=complib, complevel=complevel, legacy_mode=legacy_mode)
- print('Finished shuffling. Output information:')
- print('---------------------------------------')
- print('The output file contains the following datasets:')
- for dataset_name in output_file_shuffled:
- print('Dataset ' + dataset_name + ' with the following shape, dtype and chunks '
- '(first argument is the chunksize in axis_0): \n' + str(output_file_shuffled[dataset_name].shape)
- + ' ; ' + str(output_file_shuffled[dataset_name].dtype) + ' ; '
- + str(output_file_shuffled[dataset_name].chunks))
-
- output_file_shuffled.close()
-
-
-if __name__ == '__main__':
- main()
diff --git a/orcasong_contrib/data_tools/split_conc.py b/orcasong_contrib/data_tools/split_conc.py
deleted file mode 100644
index 1a7aed404f39b0d61b0c42da5c9400519c8a18b4..0000000000000000000000000000000000000000
--- a/orcasong_contrib/data_tools/split_conc.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""
-Distribute filepaths among lists, so that they can be concatenated to a
-given number of files, and with a train/val split.
-
-Main function: get_split
- The output will be a list with dicts containing a name for the concatenated
- file, and the paths of the individual files that will be concatenated.
- This can be handed over to concatenate_h5 in orcasong.
-
-Example:
- For a folder with 4 files named file_0.h5 to file_3.h5 and a 50:50 split
- between train/val:
-
- get_split(folder,
- outfile_basestr='test',
- n_train_files=1,
- n_val_files=1,
- train_frac=0.5)
-
- [
- {
- 'file_list': array(['file_2.h5', file_1.h5]),
- 'output_filepath': 'test_train_0.h5',
- 'is_train': True,
- },
- {
- 'file_list': array(['file_4.h5', 'file_0.h5']),
- 'output_filepath': 'test_val_0.h5',
- 'is_train': False,
- },
- ]
-
-"""
-import os
-import numpy as np
-
-__author__ = 'Stefan Reck'
-
-
-def get_files(folder):
- """
- Get pathes of all h5 files in given folder.
- """
- infiles = os.listdir(folder)
- infiles.sort()
-
- infile_paths = []
- for infile in infiles:
- if infile.endswith(".h5"):
- infile_paths.append(os.path.join(folder, infile))
-
- return np.array(infile_paths)
-
-
-def split_path_list(files, train_frac, n_train_files, n_val_files):
- """
- Get train and val files split according to given fraction, and
- distributed over given number of files.
-
- Parameters
- ----------
- files : List
- The files.
- train_frac : float
- The fraction of files.
- n_train_files : int
- Total number of resulting train files.
- n_val_files : int
- Total number of resulting val files.
-
- Returns
- -------
- job_files_train : ndarray
- The train files. They are chosen randomly from the files list.
- The total number of files is the given fraction of the input files.
- job_files_val : ndarray
- The val files, similar to the train files.
-
- """
- if n_train_files < 1 or n_val_files < 1:
- raise ValueError("Need at least 1 file for train and val.")
-
- order = np.arange(len(files))
- np.random.shuffle(order)
-
- len_train_files = int(len(files) * train_frac)
- train_files = files[order[:len_train_files]]
- val_files = files[order[len_train_files:]]
-
- job_files_train = np.array_split(train_files, n_train_files)
- job_files_val = np.array_split(val_files, n_val_files)
-
- for fs in job_files_train:
- if len(fs) == 0:
- raise ValueError("No files for an output train file!")
-
- for fs in job_files_val:
- if len(fs) == 0:
- raise ValueError("No files for an output val file!")
-
- return job_files_train, job_files_val
-
-
-def get_split(folder, outfile_basestr, n_train_files=1, n_val_files=1,
- train_frac=0.8, seed=64):
- """
- Distribute filepaths among lists (see doc at top of file).
-
- The files in each train or val file will be drawn randomly from the
- available files. Each train or val files will be created by its own
- seperately submitted job.
-
- Parameters
- ----------
- folder : str
- Containing the files to concat.
- n_train_files : int
- Total number of resulting train files.
- n_val_files : int
- Total number of resulting val files.
- outfile_basestr : str
- Path and a base for the name. "train"/"val" and a file number will
- get automatically attached to the name.
- train_frac : float
- The fraction of files in the train set.
- seed : int, optional
- Seed for the random stuff.
-
- Returns
- -------
- jobs : dict
- Contains the arguments for the concatenate script.
-
- """
- if seed is not None:
- np.random.seed(seed)
-
- files = get_files(folder)
- job_files_train, job_files_val = split_path_list(files, train_frac, n_train_files, n_val_files)
-
- jobs = []
-
- for i, job_files in enumerate(job_files_train):
- output_filepath = "{}_train_{}.h5".format(outfile_basestr, i)
- job_dict = {
- "file_list": job_files,
- "output_filepath": output_filepath,
- "is_train": True,
- }
- jobs.append(job_dict)
-
- for i, job_files in enumerate(job_files_val):
- output_filepath = "{}_val_{}.h5".format(outfile_basestr, i)
- job_dict = {
- "file_list": job_files,
- "output_filepath": output_filepath,
- "is_train": False,
- }
- jobs.append(job_dict)
-
- return jobs
diff --git a/orcasong_contrib/detx_files/orca_115strings_av23min20mhorizontal_18OMs_alt9mvertical_v1.detx b/orcasong_contrib/detx_files/orca_115strings_av23min20mhorizontal_18OMs_alt9mvertical_v1.detx
deleted file mode 100644
index 5ed3163cad5922ccc82b403b3a7bc67136a1c6ec..0000000000000000000000000000000000000000
--- a/orcasong_contrib/detx_files/orca_115strings_av23min20mhorizontal_18OMs_alt9mvertical_v1.detx
+++ /dev/null
@@ -1,66241 +0,0 @@
--1 2070
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-1830 -53.872 98.766 150.7 -0.0 -0.0 -1.0 0.0
-1831 -53.78 98.819 150.731 0.46 0.266 -0.847 0.0
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-61 4 12 31
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-1862 -53.78 98.819 139.831 0.46 0.266 -0.847 0.0
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-62 4 11 31
-1892 -53.872 98.766 130.4 -0.0 -0.0 -1.0 0.0
-1893 -53.78 98.819 130.431 0.46 0.266 -0.847 0.0
-1894 -53.872 98.872 130.431 0.0 0.531 -0.847 0.0
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-1899 -53.706 98.766 130.489 0.831 0.0 -0.556 0.0
-1900 -53.789 98.91 130.489 0.415 0.72 -0.556 0.0
-1901 -53.955 98.91 130.489 -0.415 0.72 -0.556 0.0
-1902 -54.038 98.766 130.489 -0.831 -0.0 -0.556 0.0
-1903 -53.955 98.622 130.489 -0.415 -0.72 -0.556 0.0
-1904 -53.789 98.622 130.489 0.415 -0.72 -0.556 0.0
-1905 -53.707 98.862 130.541 0.827 0.478 -0.296 0.0
-1906 -53.872 98.957 130.541 0.0 0.955 -0.296 0.0
-1907 -54.037 98.862 130.541 -0.827 0.478 -0.296 0.0
-1908 -54.037 98.67 130.541 -0.827 -0.478 -0.296 0.0
-1909 -53.872 98.575 130.541 0.0 -0.955 -0.296 0.0
-1910 -53.707 98.67 130.541 0.827 -0.478 -0.296 0.0
-1911 -53.681 98.766 130.659 0.955 0.0 0.296 0.0
-1912 -53.776 98.931 130.659 0.478 0.827 0.296 0.0
-1913 -53.968 98.931 130.659 -0.478 0.827 0.296 0.0
-1914 -54.063 98.766 130.659 -0.955 -0.0 0.296 0.0
-1915 -53.968 98.601 130.659 -0.478 -0.827 0.296 0.0
-1916 -53.776 98.601 130.659 0.478 -0.827 0.296 0.0
-1917 -53.728 98.849 130.711 0.719 0.415 0.558 0.0
-1918 -53.872 98.932 130.711 0.0 0.83 0.558 0.0
-1919 -54.016 98.849 130.711 -0.719 0.415 0.558 0.0
-1920 -54.016 98.683 130.711 -0.719 -0.415 0.558 0.0
-1921 -53.872 98.6 130.711 0.0 -0.83 0.558 0.0
-1922 -53.728 98.683 130.711 0.719 -0.415 0.558 0.0
-63 4 10 31
-1923 -53.872 98.766 121.7 -0.0 -0.0 -1.0 0.0
-1924 -53.78 98.819 121.731 0.46 0.266 -0.847 0.0
-1925 -53.872 98.872 121.731 0.0 0.531 -0.847 0.0
-1926 -53.964 98.819 121.731 -0.46 0.266 -0.847 0.0
-1927 -53.964 98.713 121.731 -0.46 -0.266 -0.847 0.0
-1928 -53.872 98.66 121.731 0.0 -0.531 -0.847 0.0
-1929 -53.78 98.713 121.731 0.46 -0.266 -0.847 0.0
-1930 -53.706 98.766 121.789 0.831 0.0 -0.556 0.0
-1931 -53.789 98.91 121.789 0.415 0.72 -0.556 0.0
-1932 -53.955 98.91 121.789 -0.415 0.72 -0.556 0.0
-1933 -54.038 98.766 121.789 -0.831 -0.0 -0.556 0.0
-1934 -53.955 98.622 121.789 -0.415 -0.72 -0.556 0.0
-1935 -53.789 98.622 121.789 0.415 -0.72 -0.556 0.0
-1936 -53.707 98.862 121.841 0.827 0.478 -0.296 0.0
-1937 -53.872 98.957 121.841 0.0 0.955 -0.296 0.0
-1938 -54.037 98.862 121.841 -0.827 0.478 -0.296 0.0
-1939 -54.037 98.67 121.841 -0.827 -0.478 -0.296 0.0
-1940 -53.872 98.575 121.841 0.0 -0.955 -0.296 0.0
-1941 -53.707 98.67 121.841 0.827 -0.478 -0.296 0.0
-1942 -53.681 98.766 121.959 0.955 0.0 0.296 0.0
-1943 -53.776 98.931 121.959 0.478 0.827 0.296 0.0
-1944 -53.968 98.931 121.959 -0.478 0.827 0.296 0.0
-1945 -54.063 98.766 121.959 -0.955 -0.0 0.296 0.0
-1946 -53.968 98.601 121.959 -0.478 -0.827 0.296 0.0
-1947 -53.776 98.601 121.959 0.478 -0.827 0.296 0.0
-1948 -53.728 98.849 122.011 0.719 0.415 0.558 0.0
-1949 -53.872 98.932 122.011 0.0 0.83 0.558 0.0
-1950 -54.016 98.849 122.011 -0.719 0.415 0.558 0.0
-1951 -54.016 98.683 122.011 -0.719 -0.415 0.558 0.0
-1952 -53.872 98.6 122.011 0.0 -0.83 0.558 0.0
-1953 -53.728 98.683 122.011 0.719 -0.415 0.558 0.0
-64 4 9 31
-1954 -53.872 98.766 112.3 -0.0 -0.0 -1.0 0.0
-1955 -53.78 98.819 112.331 0.46 0.266 -0.847 0.0
-1956 -53.872 98.872 112.331 0.0 0.531 -0.847 0.0
-1957 -53.964 98.819 112.331 -0.46 0.266 -0.847 0.0
-1958 -53.964 98.713 112.331 -0.46 -0.266 -0.847 0.0
-1959 -53.872 98.66 112.331 0.0 -0.531 -0.847 0.0
-1960 -53.78 98.713 112.331 0.46 -0.266 -0.847 0.0
-1961 -53.706 98.766 112.389 0.831 0.0 -0.556 0.0
-1962 -53.789 98.91 112.389 0.415 0.72 -0.556 0.0
-1963 -53.955 98.91 112.389 -0.415 0.72 -0.556 0.0
-1964 -54.038 98.766 112.389 -0.831 -0.0 -0.556 0.0
-1965 -53.955 98.622 112.389 -0.415 -0.72 -0.556 0.0
-1966 -53.789 98.622 112.389 0.415 -0.72 -0.556 0.0
-1967 -53.707 98.862 112.441 0.827 0.478 -0.296 0.0
-1968 -53.872 98.957 112.441 0.0 0.955 -0.296 0.0
-1969 -54.037 98.862 112.441 -0.827 0.478 -0.296 0.0
-1970 -54.037 98.67 112.441 -0.827 -0.478 -0.296 0.0
-1971 -53.872 98.575 112.441 0.0 -0.955 -0.296 0.0
-1972 -53.707 98.67 112.441 0.827 -0.478 -0.296 0.0
-1973 -53.681 98.766 112.559 0.955 0.0 0.296 0.0
-1974 -53.776 98.931 112.559 0.478 0.827 0.296 0.0
-1975 -53.968 98.931 112.559 -0.478 0.827 0.296 0.0
-1976 -54.063 98.766 112.559 -0.955 -0.0 0.296 0.0
-1977 -53.968 98.601 112.559 -0.478 -0.827 0.296 0.0
-1978 -53.776 98.601 112.559 0.478 -0.827 0.296 0.0
-1979 -53.728 98.849 112.611 0.719 0.415 0.558 0.0
-1980 -53.872 98.932 112.611 0.0 0.83 0.558 0.0
-1981 -54.016 98.849 112.611 -0.719 0.415 0.558 0.0
-1982 -54.016 98.683 112.611 -0.719 -0.415 0.558 0.0
-1983 -53.872 98.6 112.611 0.0 -0.83 0.558 0.0
-1984 -53.728 98.683 112.611 0.719 -0.415 0.558 0.0
-65 4 8 31
-1985 -53.872 98.766 103.6 -0.0 -0.0 -1.0 0.0
-1986 -53.78 98.819 103.631 0.46 0.266 -0.847 0.0
-1987 -53.872 98.872 103.631 0.0 0.531 -0.847 0.0
-1988 -53.964 98.819 103.631 -0.46 0.266 -0.847 0.0
-1989 -53.964 98.713 103.631 -0.46 -0.266 -0.847 0.0
-1990 -53.872 98.66 103.631 0.0 -0.531 -0.847 0.0
-1991 -53.78 98.713 103.631 0.46 -0.266 -0.847 0.0
-1992 -53.706 98.766 103.689 0.831 0.0 -0.556 0.0
-1993 -53.789 98.91 103.689 0.415 0.72 -0.556 0.0
-1994 -53.955 98.91 103.689 -0.415 0.72 -0.556 0.0
-1995 -54.038 98.766 103.689 -0.831 -0.0 -0.556 0.0
-1996 -53.955 98.622 103.689 -0.415 -0.72 -0.556 0.0
-1997 -53.789 98.622 103.689 0.415 -0.72 -0.556 0.0
-1998 -53.707 98.862 103.741 0.827 0.478 -0.296 0.0
-1999 -53.872 98.957 103.741 0.0 0.955 -0.296 0.0
-2000 -54.037 98.862 103.741 -0.827 0.478 -0.296 0.0
-2001 -54.037 98.67 103.741 -0.827 -0.478 -0.296 0.0
-2002 -53.872 98.575 103.741 0.0 -0.955 -0.296 0.0
-2003 -53.707 98.67 103.741 0.827 -0.478 -0.296 0.0
-2004 -53.681 98.766 103.859 0.955 0.0 0.296 0.0
-2005 -53.776 98.931 103.859 0.478 0.827 0.296 0.0
-2006 -53.968 98.931 103.859 -0.478 0.827 0.296 0.0
-2007 -54.063 98.766 103.859 -0.955 -0.0 0.296 0.0
-2008 -53.968 98.601 103.859 -0.478 -0.827 0.296 0.0
-2009 -53.776 98.601 103.859 0.478 -0.827 0.296 0.0
-2010 -53.728 98.849 103.911 0.719 0.415 0.558 0.0
-2011 -53.872 98.932 103.911 0.0 0.83 0.558 0.0
-2012 -54.016 98.849 103.911 -0.719 0.415 0.558 0.0
-2013 -54.016 98.683 103.911 -0.719 -0.415 0.558 0.0
-2014 -53.872 98.6 103.911 0.0 -0.83 0.558 0.0
-2015 -53.728 98.683 103.911 0.719 -0.415 0.558 0.0
-66 4 7 31
-2016 -53.872 98.766 94.2 -0.0 -0.0 -1.0 0.0
-2017 -53.78 98.819 94.231 0.46 0.266 -0.847 0.0
-2018 -53.872 98.872 94.231 0.0 0.531 -0.847 0.0
-2019 -53.964 98.819 94.231 -0.46 0.266 -0.847 0.0
-2020 -53.964 98.713 94.231 -0.46 -0.266 -0.847 0.0
-2021 -53.872 98.66 94.231 0.0 -0.531 -0.847 0.0
-2022 -53.78 98.713 94.231 0.46 -0.266 -0.847 0.0
-2023 -53.706 98.766 94.289 0.831 0.0 -0.556 0.0
-2024 -53.789 98.91 94.289 0.415 0.72 -0.556 0.0
-2025 -53.955 98.91 94.289 -0.415 0.72 -0.556 0.0
-2026 -54.038 98.766 94.289 -0.831 -0.0 -0.556 0.0
-2027 -53.955 98.622 94.289 -0.415 -0.72 -0.556 0.0
-2028 -53.789 98.622 94.289 0.415 -0.72 -0.556 0.0
-2029 -53.707 98.862 94.341 0.827 0.478 -0.296 0.0
-2030 -53.872 98.957 94.341 0.0 0.955 -0.296 0.0
-2031 -54.037 98.862 94.341 -0.827 0.478 -0.296 0.0
-2032 -54.037 98.67 94.341 -0.827 -0.478 -0.296 0.0
-2033 -53.872 98.575 94.341 0.0 -0.955 -0.296 0.0
-2034 -53.707 98.67 94.341 0.827 -0.478 -0.296 0.0
-2035 -53.681 98.766 94.459 0.955 0.0 0.296 0.0
-2036 -53.776 98.931 94.459 0.478 0.827 0.296 0.0
-2037 -53.968 98.931 94.459 -0.478 0.827 0.296 0.0
-2038 -54.063 98.766 94.459 -0.955 -0.0 0.296 0.0
-2039 -53.968 98.601 94.459 -0.478 -0.827 0.296 0.0
-2040 -53.776 98.601 94.459 0.478 -0.827 0.296 0.0
-2041 -53.728 98.849 94.511 0.719 0.415 0.558 0.0
-2042 -53.872 98.932 94.511 0.0 0.83 0.558 0.0
-2043 -54.016 98.849 94.511 -0.719 0.415 0.558 0.0
-2044 -54.016 98.683 94.511 -0.719 -0.415 0.558 0.0
-2045 -53.872 98.6 94.511 0.0 -0.83 0.558 0.0
-2046 -53.728 98.683 94.511 0.719 -0.415 0.558 0.0
-67 4 6 31
-2047 -53.872 98.766 83.3 -0.0 -0.0 -1.0 0.0
-2048 -53.78 98.819 83.331 0.46 0.266 -0.847 0.0
-2049 -53.872 98.872 83.331 0.0 0.531 -0.847 0.0
-2050 -53.964 98.819 83.331 -0.46 0.266 -0.847 0.0
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-2054 -53.706 98.766 83.389 0.831 0.0 -0.556 0.0
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-2060 -53.707 98.862 83.441 0.827 0.478 -0.296 0.0
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-2065 -53.707 98.67 83.441 0.827 -0.478 -0.296 0.0
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-2072 -53.728 98.849 83.611 0.719 0.415 0.558 0.0
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-2076 -53.872 98.6 83.611 0.0 -0.83 0.558 0.0
-2077 -53.728 98.683 83.611 0.719 -0.415 0.558 0.0
-68 4 5 31
-2078 -53.872 98.766 73.9 -0.0 -0.0 -1.0 0.0
-2079 -53.78 98.819 73.931 0.46 0.266 -0.847 0.0
-2080 -53.872 98.872 73.931 0.0 0.531 -0.847 0.0
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-2082 -53.964 98.713 73.931 -0.46 -0.266 -0.847 0.0
-2083 -53.872 98.66 73.931 0.0 -0.531 -0.847 0.0
-2084 -53.78 98.713 73.931 0.46 -0.266 -0.847 0.0
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-1354 76 15 31
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-41945 35.701 14.884 168.831 0.46 0.266 -0.847 0.0
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-1355 76 14 31
-41975 35.609 14.831 160.1 -0.0 -0.0 -1.0 0.0
-41976 35.701 14.884 160.131 0.46 0.266 -0.847 0.0
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-42005 35.753 14.748 160.411 0.719 -0.415 0.558 0.0
-1356 76 13 31
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-42007 35.701 14.884 150.731 0.46 0.266 -0.847 0.0
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-1359 76 10 31
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-1674 93 1 31
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-1677 94 16 31
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-1678 94 15 31
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-1714 96 15 31
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-1716 96 13 31
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-1717 96 12 31
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-1718 96 11 31
-53228 2.91 119.964 130.4 -0.0 -0.0 -1.0 0.0
-53229 3.002 120.017 130.431 0.46 0.266 -0.847 0.0
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-1719 96 10 31
-53259 2.91 119.964 121.7 -0.0 -0.0 -1.0 0.0
-53260 3.002 120.017 121.731 0.46 0.266 -0.847 0.0
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-1720 96 9 31
-53290 2.91 119.964 112.3 -0.0 -0.0 -1.0 0.0
-53291 3.002 120.017 112.331 0.46 0.266 -0.847 0.0
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-1721 96 8 31
-53321 2.91 119.964 103.6 -0.0 -0.0 -1.0 0.0
-53322 3.002 120.017 103.631 0.46 0.266 -0.847 0.0
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-1722 96 7 31
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-1723 96 6 31
-53383 2.91 119.964 83.3 -0.0 -0.0 -1.0 0.0
-53384 3.002 120.017 83.331 0.46 0.266 -0.847 0.0
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-1724 96 5 31
-53414 2.91 119.964 73.9 -0.0 -0.0 -1.0 0.0
-53415 3.002 120.017 73.931 0.46 0.266 -0.847 0.0
-53416 2.91 120.07 73.931 0.0 0.531 -0.847 0.0
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-60218 89.521 -45.855 47.241 -0.827 0.478 -0.296 0.0
-60219 89.521 -46.047 47.241 -0.827 -0.478 -0.296 0.0
-60220 89.686 -46.142 47.241 0.0 -0.955 -0.296 0.0
-60221 89.851 -46.047 47.241 0.827 -0.478 -0.296 0.0
-60222 89.877 -45.951 47.359 0.955 0.0 0.296 0.0
-60223 89.782 -45.786 47.359 0.478 0.827 0.296 0.0
-60224 89.59 -45.786 47.359 -0.478 0.827 0.296 0.0
-60225 89.495 -45.951 47.359 -0.955 -0.0 0.296 0.0
-60226 89.59 -46.116 47.359 -0.478 -0.827 0.296 0.0
-60227 89.782 -46.116 47.359 0.478 -0.827 0.296 0.0
-60228 89.83 -45.868 47.411 0.719 0.415 0.558 0.0
-60229 89.686 -45.785 47.411 0.0 0.83 0.558 0.0
-60230 89.542 -45.868 47.411 -0.719 0.415 0.558 0.0
-60231 89.542 -46.034 47.411 -0.719 -0.415 0.558 0.0
-60232 89.686 -46.117 47.411 0.0 -0.83 0.558 0.0
-60233 89.83 -46.034 47.411 0.719 -0.415 0.558 0.0
-1944 108 1 31
-60234 89.686 -45.951 37.7 -0.0 -0.0 -1.0 0.0
-60235 89.778 -45.898 37.731 0.46 0.266 -0.847 0.0
-60236 89.686 -45.845 37.731 0.0 0.531 -0.847 0.0
-60237 89.594 -45.898 37.731 -0.46 0.266 -0.847 0.0
-60238 89.594 -46.004 37.731 -0.46 -0.266 -0.847 0.0
-60239 89.686 -46.057 37.731 0.0 -0.531 -0.847 0.0
-60240 89.778 -46.004 37.731 0.46 -0.266 -0.847 0.0
-60241 89.852 -45.951 37.789 0.831 0.0 -0.556 0.0
-60242 89.769 -45.807 37.789 0.415 0.72 -0.556 0.0
-60243 89.603 -45.807 37.789 -0.415 0.72 -0.556 0.0
-60244 89.52 -45.951 37.789 -0.831 -0.0 -0.556 0.0
-60245 89.603 -46.095 37.789 -0.415 -0.72 -0.556 0.0
-60246 89.769 -46.095 37.789 0.415 -0.72 -0.556 0.0
-60247 89.851 -45.855 37.841 0.827 0.478 -0.296 0.0
-60248 89.686 -45.76 37.841 0.0 0.955 -0.296 0.0
-60249 89.521 -45.855 37.841 -0.827 0.478 -0.296 0.0
-60250 89.521 -46.047 37.841 -0.827 -0.478 -0.296 0.0
-60251 89.686 -46.142 37.841 0.0 -0.955 -0.296 0.0
-60252 89.851 -46.047 37.841 0.827 -0.478 -0.296 0.0
-60253 89.877 -45.951 37.959 0.955 0.0 0.296 0.0
-60254 89.782 -45.786 37.959 0.478 0.827 0.296 0.0
-60255 89.59 -45.786 37.959 -0.478 0.827 0.296 0.0
-60256 89.495 -45.951 37.959 -0.955 -0.0 0.296 0.0
-60257 89.59 -46.116 37.959 -0.478 -0.827 0.296 0.0
-60258 89.782 -46.116 37.959 0.478 -0.827 0.296 0.0
-60259 89.83 -45.868 38.011 0.719 0.415 0.558 0.0
-60260 89.686 -45.785 38.011 0.0 0.83 0.558 0.0
-60261 89.542 -45.868 38.011 -0.719 0.415 0.558 0.0
-60262 89.542 -46.034 38.011 -0.719 -0.415 0.558 0.0
-60263 89.686 -46.117 38.011 0.0 -0.83 0.558 0.0
-60264 89.83 -46.034 38.011 0.719 -0.415 0.558 0.0
-1945 109 18 31
-60265 35.532 -68.022 196.3 -0.0 -0.0 -1.0 0.0
-60266 35.624 -67.969 196.331 0.46 0.266 -0.847 0.0
-60267 35.532 -67.916 196.331 0.0 0.531 -0.847 0.0
-60268 35.44 -67.969 196.331 -0.46 0.266 -0.847 0.0
-60269 35.44 -68.075 196.331 -0.46 -0.266 -0.847 0.0
-60270 35.532 -68.128 196.331 0.0 -0.531 -0.847 0.0
-60271 35.624 -68.075 196.331 0.46 -0.266 -0.847 0.0
-60272 35.698 -68.022 196.389 0.831 0.0 -0.556 0.0
-60273 35.615 -67.878 196.389 0.415 0.72 -0.556 0.0
-60274 35.449 -67.878 196.389 -0.415 0.72 -0.556 0.0
-60275 35.366 -68.022 196.389 -0.831 -0.0 -0.556 0.0
-60276 35.449 -68.166 196.389 -0.415 -0.72 -0.556 0.0
-60277 35.615 -68.166 196.389 0.415 -0.72 -0.556 0.0
-60278 35.697 -67.926 196.441 0.827 0.478 -0.296 0.0
-60279 35.532 -67.831 196.441 0.0 0.955 -0.296 0.0
-60280 35.367 -67.926 196.441 -0.827 0.478 -0.296 0.0
-60281 35.367 -68.118 196.441 -0.827 -0.478 -0.296 0.0
-60282 35.532 -68.213 196.441 0.0 -0.955 -0.296 0.0
-60283 35.697 -68.118 196.441 0.827 -0.478 -0.296 0.0
-60284 35.723 -68.022 196.559 0.955 0.0 0.296 0.0
-60285 35.628 -67.857 196.559 0.478 0.827 0.296 0.0
-60286 35.436 -67.857 196.559 -0.478 0.827 0.296 0.0
-60287 35.341 -68.022 196.559 -0.955 -0.0 0.296 0.0
-60288 35.436 -68.187 196.559 -0.478 -0.827 0.296 0.0
-60289 35.628 -68.187 196.559 0.478 -0.827 0.296 0.0
-60290 35.676 -67.939 196.611 0.719 0.415 0.558 0.0
-60291 35.532 -67.856 196.611 0.0 0.83 0.558 0.0
-60292 35.388 -67.939 196.611 -0.719 0.415 0.558 0.0
-60293 35.388 -68.105 196.611 -0.719 -0.415 0.558 0.0
-60294 35.532 -68.188 196.611 0.0 -0.83 0.558 0.0
-60295 35.676 -68.105 196.611 0.719 -0.415 0.558 0.0
-1946 109 17 31
-60296 35.532 -68.022 186.9 -0.0 -0.0 -1.0 0.0
-60297 35.624 -67.969 186.931 0.46 0.266 -0.847 0.0
-60298 35.532 -67.916 186.931 0.0 0.531 -0.847 0.0
-60299 35.44 -67.969 186.931 -0.46 0.266 -0.847 0.0
-60300 35.44 -68.075 186.931 -0.46 -0.266 -0.847 0.0
-60301 35.532 -68.128 186.931 0.0 -0.531 -0.847 0.0
-60302 35.624 -68.075 186.931 0.46 -0.266 -0.847 0.0
-60303 35.698 -68.022 186.989 0.831 0.0 -0.556 0.0
-60304 35.615 -67.878 186.989 0.415 0.72 -0.556 0.0
-60305 35.449 -67.878 186.989 -0.415 0.72 -0.556 0.0
-60306 35.366 -68.022 186.989 -0.831 -0.0 -0.556 0.0
-60307 35.449 -68.166 186.989 -0.415 -0.72 -0.556 0.0
-60308 35.615 -68.166 186.989 0.415 -0.72 -0.556 0.0
-60309 35.697 -67.926 187.041 0.827 0.478 -0.296 0.0
-60310 35.532 -67.831 187.041 0.0 0.955 -0.296 0.0
-60311 35.367 -67.926 187.041 -0.827 0.478 -0.296 0.0
-60312 35.367 -68.118 187.041 -0.827 -0.478 -0.296 0.0
-60313 35.532 -68.213 187.041 0.0 -0.955 -0.296 0.0
-60314 35.697 -68.118 187.041 0.827 -0.478 -0.296 0.0
-60315 35.723 -68.022 187.159 0.955 0.0 0.296 0.0
-60316 35.628 -67.857 187.159 0.478 0.827 0.296 0.0
-60317 35.436 -67.857 187.159 -0.478 0.827 0.296 0.0
-60318 35.341 -68.022 187.159 -0.955 -0.0 0.296 0.0
-60319 35.436 -68.187 187.159 -0.478 -0.827 0.296 0.0
-60320 35.628 -68.187 187.159 0.478 -0.827 0.296 0.0
-60321 35.676 -67.939 187.211 0.719 0.415 0.558 0.0
-60322 35.532 -67.856 187.211 0.0 0.83 0.558 0.0
-60323 35.388 -67.939 187.211 -0.719 0.415 0.558 0.0
-60324 35.388 -68.105 187.211 -0.719 -0.415 0.558 0.0
-60325 35.532 -68.188 187.211 0.0 -0.83 0.558 0.0
-60326 35.676 -68.105 187.211 0.719 -0.415 0.558 0.0
-1947 109 16 31
-60327 35.532 -68.022 178.2 -0.0 -0.0 -1.0 0.0
-60328 35.624 -67.969 178.231 0.46 0.266 -0.847 0.0
-60329 35.532 -67.916 178.231 0.0 0.531 -0.847 0.0
-60330 35.44 -67.969 178.231 -0.46 0.266 -0.847 0.0
-60331 35.44 -68.075 178.231 -0.46 -0.266 -0.847 0.0
-60332 35.532 -68.128 178.231 0.0 -0.531 -0.847 0.0
-60333 35.624 -68.075 178.231 0.46 -0.266 -0.847 0.0
-60334 35.698 -68.022 178.289 0.831 0.0 -0.556 0.0
-60335 35.615 -67.878 178.289 0.415 0.72 -0.556 0.0
-60336 35.449 -67.878 178.289 -0.415 0.72 -0.556 0.0
-60337 35.366 -68.022 178.289 -0.831 -0.0 -0.556 0.0
-60338 35.449 -68.166 178.289 -0.415 -0.72 -0.556 0.0
-60339 35.615 -68.166 178.289 0.415 -0.72 -0.556 0.0
-60340 35.697 -67.926 178.341 0.827 0.478 -0.296 0.0
-60341 35.532 -67.831 178.341 0.0 0.955 -0.296 0.0
-60342 35.367 -67.926 178.341 -0.827 0.478 -0.296 0.0
-60343 35.367 -68.118 178.341 -0.827 -0.478 -0.296 0.0
-60344 35.532 -68.213 178.341 0.0 -0.955 -0.296 0.0
-60345 35.697 -68.118 178.341 0.827 -0.478 -0.296 0.0
-60346 35.723 -68.022 178.459 0.955 0.0 0.296 0.0
-60347 35.628 -67.857 178.459 0.478 0.827 0.296 0.0
-60348 35.436 -67.857 178.459 -0.478 0.827 0.296 0.0
-60349 35.341 -68.022 178.459 -0.955 -0.0 0.296 0.0
-60350 35.436 -68.187 178.459 -0.478 -0.827 0.296 0.0
-60351 35.628 -68.187 178.459 0.478 -0.827 0.296 0.0
-60352 35.676 -67.939 178.511 0.719 0.415 0.558 0.0
-60353 35.532 -67.856 178.511 0.0 0.83 0.558 0.0
-60354 35.388 -67.939 178.511 -0.719 0.415 0.558 0.0
-60355 35.388 -68.105 178.511 -0.719 -0.415 0.558 0.0
-60356 35.532 -68.188 178.511 0.0 -0.83 0.558 0.0
-60357 35.676 -68.105 178.511 0.719 -0.415 0.558 0.0
-1948 109 15 31
-60358 35.532 -68.022 168.8 -0.0 -0.0 -1.0 0.0
-60359 35.624 -67.969 168.831 0.46 0.266 -0.847 0.0
-60360 35.532 -67.916 168.831 0.0 0.531 -0.847 0.0
-60361 35.44 -67.969 168.831 -0.46 0.266 -0.847 0.0
-60362 35.44 -68.075 168.831 -0.46 -0.266 -0.847 0.0
-60363 35.532 -68.128 168.831 0.0 -0.531 -0.847 0.0
-60364 35.624 -68.075 168.831 0.46 -0.266 -0.847 0.0
-60365 35.698 -68.022 168.889 0.831 0.0 -0.556 0.0
-60366 35.615 -67.878 168.889 0.415 0.72 -0.556 0.0
-60367 35.449 -67.878 168.889 -0.415 0.72 -0.556 0.0
-60368 35.366 -68.022 168.889 -0.831 -0.0 -0.556 0.0
-60369 35.449 -68.166 168.889 -0.415 -0.72 -0.556 0.0
-60370 35.615 -68.166 168.889 0.415 -0.72 -0.556 0.0
-60371 35.697 -67.926 168.941 0.827 0.478 -0.296 0.0
-60372 35.532 -67.831 168.941 0.0 0.955 -0.296 0.0
-60373 35.367 -67.926 168.941 -0.827 0.478 -0.296 0.0
-60374 35.367 -68.118 168.941 -0.827 -0.478 -0.296 0.0
-60375 35.532 -68.213 168.941 0.0 -0.955 -0.296 0.0
-60376 35.697 -68.118 168.941 0.827 -0.478 -0.296 0.0
-60377 35.723 -68.022 169.059 0.955 0.0 0.296 0.0
-60378 35.628 -67.857 169.059 0.478 0.827 0.296 0.0
-60379 35.436 -67.857 169.059 -0.478 0.827 0.296 0.0
-60380 35.341 -68.022 169.059 -0.955 -0.0 0.296 0.0
-60381 35.436 -68.187 169.059 -0.478 -0.827 0.296 0.0
-60382 35.628 -68.187 169.059 0.478 -0.827 0.296 0.0
-60383 35.676 -67.939 169.111 0.719 0.415 0.558 0.0
-60384 35.532 -67.856 169.111 0.0 0.83 0.558 0.0
-60385 35.388 -67.939 169.111 -0.719 0.415 0.558 0.0
-60386 35.388 -68.105 169.111 -0.719 -0.415 0.558 0.0
-60387 35.532 -68.188 169.111 0.0 -0.83 0.558 0.0
-60388 35.676 -68.105 169.111 0.719 -0.415 0.558 0.0
-1949 109 14 31
-60389 35.532 -68.022 160.1 -0.0 -0.0 -1.0 0.0
-60390 35.624 -67.969 160.131 0.46 0.266 -0.847 0.0
-60391 35.532 -67.916 160.131 0.0 0.531 -0.847 0.0
-60392 35.44 -67.969 160.131 -0.46 0.266 -0.847 0.0
-60393 35.44 -68.075 160.131 -0.46 -0.266 -0.847 0.0
-60394 35.532 -68.128 160.131 0.0 -0.531 -0.847 0.0
-60395 35.624 -68.075 160.131 0.46 -0.266 -0.847 0.0
-60396 35.698 -68.022 160.189 0.831 0.0 -0.556 0.0
-60397 35.615 -67.878 160.189 0.415 0.72 -0.556 0.0
-60398 35.449 -67.878 160.189 -0.415 0.72 -0.556 0.0
-60399 35.366 -68.022 160.189 -0.831 -0.0 -0.556 0.0
-60400 35.449 -68.166 160.189 -0.415 -0.72 -0.556 0.0
-60401 35.615 -68.166 160.189 0.415 -0.72 -0.556 0.0
-60402 35.697 -67.926 160.241 0.827 0.478 -0.296 0.0
-60403 35.532 -67.831 160.241 0.0 0.955 -0.296 0.0
-60404 35.367 -67.926 160.241 -0.827 0.478 -0.296 0.0
-60405 35.367 -68.118 160.241 -0.827 -0.478 -0.296 0.0
-60406 35.532 -68.213 160.241 0.0 -0.955 -0.296 0.0
-60407 35.697 -68.118 160.241 0.827 -0.478 -0.296 0.0
-60408 35.723 -68.022 160.359 0.955 0.0 0.296 0.0
-60409 35.628 -67.857 160.359 0.478 0.827 0.296 0.0
-60410 35.436 -67.857 160.359 -0.478 0.827 0.296 0.0
-60411 35.341 -68.022 160.359 -0.955 -0.0 0.296 0.0
-60412 35.436 -68.187 160.359 -0.478 -0.827 0.296 0.0
-60413 35.628 -68.187 160.359 0.478 -0.827 0.296 0.0
-60414 35.676 -67.939 160.411 0.719 0.415 0.558 0.0
-60415 35.532 -67.856 160.411 0.0 0.83 0.558 0.0
-60416 35.388 -67.939 160.411 -0.719 0.415 0.558 0.0
-60417 35.388 -68.105 160.411 -0.719 -0.415 0.558 0.0
-60418 35.532 -68.188 160.411 0.0 -0.83 0.558 0.0
-60419 35.676 -68.105 160.411 0.719 -0.415 0.558 0.0
-1950 109 13 31
-60420 35.532 -68.022 150.7 -0.0 -0.0 -1.0 0.0
-60421 35.624 -67.969 150.731 0.46 0.266 -0.847 0.0
-60422 35.532 -67.916 150.731 0.0 0.531 -0.847 0.0
-60423 35.44 -67.969 150.731 -0.46 0.266 -0.847 0.0
-60424 35.44 -68.075 150.731 -0.46 -0.266 -0.847 0.0
-60425 35.532 -68.128 150.731 0.0 -0.531 -0.847 0.0
-60426 35.624 -68.075 150.731 0.46 -0.266 -0.847 0.0
-60427 35.698 -68.022 150.789 0.831 0.0 -0.556 0.0
-60428 35.615 -67.878 150.789 0.415 0.72 -0.556 0.0
-60429 35.449 -67.878 150.789 -0.415 0.72 -0.556 0.0
-60430 35.366 -68.022 150.789 -0.831 -0.0 -0.556 0.0
-60431 35.449 -68.166 150.789 -0.415 -0.72 -0.556 0.0
-60432 35.615 -68.166 150.789 0.415 -0.72 -0.556 0.0
-60433 35.697 -67.926 150.841 0.827 0.478 -0.296 0.0
-60434 35.532 -67.831 150.841 0.0 0.955 -0.296 0.0
-60435 35.367 -67.926 150.841 -0.827 0.478 -0.296 0.0
-60436 35.367 -68.118 150.841 -0.827 -0.478 -0.296 0.0
-60437 35.532 -68.213 150.841 0.0 -0.955 -0.296 0.0
-60438 35.697 -68.118 150.841 0.827 -0.478 -0.296 0.0
-60439 35.723 -68.022 150.959 0.955 0.0 0.296 0.0
-60440 35.628 -67.857 150.959 0.478 0.827 0.296 0.0
-60441 35.436 -67.857 150.959 -0.478 0.827 0.296 0.0
-60442 35.341 -68.022 150.959 -0.955 -0.0 0.296 0.0
-60443 35.436 -68.187 150.959 -0.478 -0.827 0.296 0.0
-60444 35.628 -68.187 150.959 0.478 -0.827 0.296 0.0
-60445 35.676 -67.939 151.011 0.719 0.415 0.558 0.0
-60446 35.532 -67.856 151.011 0.0 0.83 0.558 0.0
-60447 35.388 -67.939 151.011 -0.719 0.415 0.558 0.0
-60448 35.388 -68.105 151.011 -0.719 -0.415 0.558 0.0
-60449 35.532 -68.188 151.011 0.0 -0.83 0.558 0.0
-60450 35.676 -68.105 151.011 0.719 -0.415 0.558 0.0
-1951 109 12 31
-60451 35.532 -68.022 139.8 -0.0 -0.0 -1.0 0.0
-60452 35.624 -67.969 139.831 0.46 0.266 -0.847 0.0
-60453 35.532 -67.916 139.831 0.0 0.531 -0.847 0.0
-60454 35.44 -67.969 139.831 -0.46 0.266 -0.847 0.0
-60455 35.44 -68.075 139.831 -0.46 -0.266 -0.847 0.0
-60456 35.532 -68.128 139.831 0.0 -0.531 -0.847 0.0
-60457 35.624 -68.075 139.831 0.46 -0.266 -0.847 0.0
-60458 35.698 -68.022 139.889 0.831 0.0 -0.556 0.0
-60459 35.615 -67.878 139.889 0.415 0.72 -0.556 0.0
-60460 35.449 -67.878 139.889 -0.415 0.72 -0.556 0.0
-60461 35.366 -68.022 139.889 -0.831 -0.0 -0.556 0.0
-60462 35.449 -68.166 139.889 -0.415 -0.72 -0.556 0.0
-60463 35.615 -68.166 139.889 0.415 -0.72 -0.556 0.0
-60464 35.697 -67.926 139.941 0.827 0.478 -0.296 0.0
-60465 35.532 -67.831 139.941 0.0 0.955 -0.296 0.0
-60466 35.367 -67.926 139.941 -0.827 0.478 -0.296 0.0
-60467 35.367 -68.118 139.941 -0.827 -0.478 -0.296 0.0
-60468 35.532 -68.213 139.941 0.0 -0.955 -0.296 0.0
-60469 35.697 -68.118 139.941 0.827 -0.478 -0.296 0.0
-60470 35.723 -68.022 140.059 0.955 0.0 0.296 0.0
-60471 35.628 -67.857 140.059 0.478 0.827 0.296 0.0
-60472 35.436 -67.857 140.059 -0.478 0.827 0.296 0.0
-60473 35.341 -68.022 140.059 -0.955 -0.0 0.296 0.0
-60474 35.436 -68.187 140.059 -0.478 -0.827 0.296 0.0
-60475 35.628 -68.187 140.059 0.478 -0.827 0.296 0.0
-60476 35.676 -67.939 140.111 0.719 0.415 0.558 0.0
-60477 35.532 -67.856 140.111 0.0 0.83 0.558 0.0
-60478 35.388 -67.939 140.111 -0.719 0.415 0.558 0.0
-60479 35.388 -68.105 140.111 -0.719 -0.415 0.558 0.0
-60480 35.532 -68.188 140.111 0.0 -0.83 0.558 0.0
-60481 35.676 -68.105 140.111 0.719 -0.415 0.558 0.0
-1952 109 11 31
-60482 35.532 -68.022 130.4 -0.0 -0.0 -1.0 0.0
-60483 35.624 -67.969 130.431 0.46 0.266 -0.847 0.0
-60484 35.532 -67.916 130.431 0.0 0.531 -0.847 0.0
-60485 35.44 -67.969 130.431 -0.46 0.266 -0.847 0.0
-60486 35.44 -68.075 130.431 -0.46 -0.266 -0.847 0.0
-60487 35.532 -68.128 130.431 0.0 -0.531 -0.847 0.0
-60488 35.624 -68.075 130.431 0.46 -0.266 -0.847 0.0
-60489 35.698 -68.022 130.489 0.831 0.0 -0.556 0.0
-60490 35.615 -67.878 130.489 0.415 0.72 -0.556 0.0
-60491 35.449 -67.878 130.489 -0.415 0.72 -0.556 0.0
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-60512 35.676 -68.105 130.711 0.719 -0.415 0.558 0.0
-1953 109 10 31
-60513 35.532 -68.022 121.7 -0.0 -0.0 -1.0 0.0
-60514 35.624 -67.969 121.731 0.46 0.266 -0.847 0.0
-60515 35.532 -67.916 121.731 0.0 0.531 -0.847 0.0
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-60520 35.698 -68.022 121.789 0.831 0.0 -0.556 0.0
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-60522 35.449 -67.878 121.789 -0.415 0.72 -0.556 0.0
-60523 35.366 -68.022 121.789 -0.831 -0.0 -0.556 0.0
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-60525 35.615 -68.166 121.789 0.415 -0.72 -0.556 0.0
-60526 35.697 -67.926 121.841 0.827 0.478 -0.296 0.0
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-60543 35.676 -68.105 122.011 0.719 -0.415 0.558 0.0
-1954 109 9 31
-60544 35.532 -68.022 112.3 -0.0 -0.0 -1.0 0.0
-60545 35.624 -67.969 112.331 0.46 0.266 -0.847 0.0
-60546 35.532 -67.916 112.331 0.0 0.531 -0.847 0.0
-60547 35.44 -67.969 112.331 -0.46 0.266 -0.847 0.0
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-60550 35.624 -68.075 112.331 0.46 -0.266 -0.847 0.0
-60551 35.698 -68.022 112.389 0.831 0.0 -0.556 0.0
-60552 35.615 -67.878 112.389 0.415 0.72 -0.556 0.0
-60553 35.449 -67.878 112.389 -0.415 0.72 -0.556 0.0
-60554 35.366 -68.022 112.389 -0.831 -0.0 -0.556 0.0
-60555 35.449 -68.166 112.389 -0.415 -0.72 -0.556 0.0
-60556 35.615 -68.166 112.389 0.415 -0.72 -0.556 0.0
-60557 35.697 -67.926 112.441 0.827 0.478 -0.296 0.0
-60558 35.532 -67.831 112.441 0.0 0.955 -0.296 0.0
-60559 35.367 -67.926 112.441 -0.827 0.478 -0.296 0.0
-60560 35.367 -68.118 112.441 -0.827 -0.478 -0.296 0.0
-60561 35.532 -68.213 112.441 0.0 -0.955 -0.296 0.0
-60562 35.697 -68.118 112.441 0.827 -0.478 -0.296 0.0
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-60573 35.532 -68.188 112.611 0.0 -0.83 0.558 0.0
-60574 35.676 -68.105 112.611 0.719 -0.415 0.558 0.0
-1955 109 8 31
-60575 35.532 -68.022 103.6 -0.0 -0.0 -1.0 0.0
-60576 35.624 -67.969 103.631 0.46 0.266 -0.847 0.0
-60577 35.532 -67.916 103.631 0.0 0.531 -0.847 0.0
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-60579 35.44 -68.075 103.631 -0.46 -0.266 -0.847 0.0
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-60582 35.698 -68.022 103.689 0.831 0.0 -0.556 0.0
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-60584 35.449 -67.878 103.689 -0.415 0.72 -0.556 0.0
-60585 35.366 -68.022 103.689 -0.831 -0.0 -0.556 0.0
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-60588 35.697 -67.926 103.741 0.827 0.478 -0.296 0.0
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-60590 35.367 -67.926 103.741 -0.827 0.478 -0.296 0.0
-60591 35.367 -68.118 103.741 -0.827 -0.478 -0.296 0.0
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-60605 35.676 -68.105 103.911 0.719 -0.415 0.558 0.0
-1956 109 7 31
-60606 35.532 -68.022 94.2 -0.0 -0.0 -1.0 0.0
-60607 35.624 -67.969 94.231 0.46 0.266 -0.847 0.0
-60608 35.532 -67.916 94.231 0.0 0.531 -0.847 0.0
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-60622 35.367 -68.118 94.341 -0.827 -0.478 -0.296 0.0
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-60636 35.676 -68.105 94.511 0.719 -0.415 0.558 0.0
-1957 109 6 31
-60637 35.532 -68.022 83.3 -0.0 -0.0 -1.0 0.0
-60638 35.624 -67.969 83.331 0.46 0.266 -0.847 0.0
-60639 35.532 -67.916 83.331 0.0 0.531 -0.847 0.0
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-60667 35.676 -68.105 83.611 0.719 -0.415 0.558 0.0
-1958 109 5 31
-60668 35.532 -68.022 73.9 -0.0 -0.0 -1.0 0.0
-60669 35.624 -67.969 73.931 0.46 0.266 -0.847 0.0
-60670 35.532 -67.916 73.931 0.0 0.531 -0.847 0.0
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-60678 35.366 -68.022 73.989 -0.831 -0.0 -0.556 0.0
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-60681 35.697 -67.926 74.041 0.827 0.478 -0.296 0.0
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-60698 35.676 -68.105 74.211 0.719 -0.415 0.558 0.0
-1959 109 4 31
-60699 35.532 -68.022 65.2 -0.0 -0.0 -1.0 0.0
-60700 35.624 -67.969 65.231 0.46 0.266 -0.847 0.0
-60701 35.532 -67.916 65.231 0.0 0.531 -0.847 0.0
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-60729 35.676 -68.105 65.511 0.719 -0.415 0.558 0.0
-1960 109 3 31
-60730 35.532 -68.022 55.8 -0.0 -0.0 -1.0 0.0
-60731 35.624 -67.969 55.831 0.46 0.266 -0.847 0.0
-60732 35.532 -67.916 55.831 0.0 0.531 -0.847 0.0
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-60760 35.676 -68.105 56.111 0.719 -0.415 0.558 0.0
-1961 109 2 31
-60761 35.532 -68.022 47.1 -0.0 -0.0 -1.0 0.0
-60762 35.624 -67.969 47.131 0.46 0.266 -0.847 0.0
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-1962 109 1 31
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-1963 110 18 31
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-61038 57.619 -68.369 140.111 0.0 -0.83 0.558 0.0
-61039 57.763 -68.286 140.111 0.719 -0.415 0.558 0.0
-1970 110 11 31
-61040 57.619 -68.203 130.4 -0.0 -0.0 -1.0 0.0
-61041 57.711 -68.15 130.431 0.46 0.266 -0.847 0.0
-61042 57.619 -68.097 130.431 0.0 0.531 -0.847 0.0
-61043 57.527 -68.15 130.431 -0.46 0.266 -0.847 0.0
-61044 57.527 -68.256 130.431 -0.46 -0.266 -0.847 0.0
-61045 57.619 -68.309 130.431 0.0 -0.531 -0.847 0.0
-61046 57.711 -68.256 130.431 0.46 -0.266 -0.847 0.0
-61047 57.785 -68.203 130.489 0.831 0.0 -0.556 0.0
-61048 57.702 -68.059 130.489 0.415 0.72 -0.556 0.0
-61049 57.536 -68.059 130.489 -0.415 0.72 -0.556 0.0
-61050 57.453 -68.203 130.489 -0.831 -0.0 -0.556 0.0
-61051 57.536 -68.347 130.489 -0.415 -0.72 -0.556 0.0
-61052 57.702 -68.347 130.489 0.415 -0.72 -0.556 0.0
-61053 57.784 -68.107 130.541 0.827 0.478 -0.296 0.0
-61054 57.619 -68.012 130.541 0.0 0.955 -0.296 0.0
-61055 57.454 -68.107 130.541 -0.827 0.478 -0.296 0.0
-61056 57.454 -68.299 130.541 -0.827 -0.478 -0.296 0.0
-61057 57.619 -68.394 130.541 0.0 -0.955 -0.296 0.0
-61058 57.784 -68.299 130.541 0.827 -0.478 -0.296 0.0
-61059 57.81 -68.203 130.659 0.955 0.0 0.296 0.0
-61060 57.715 -68.038 130.659 0.478 0.827 0.296 0.0
-61061 57.523 -68.038 130.659 -0.478 0.827 0.296 0.0
-61062 57.428 -68.203 130.659 -0.955 -0.0 0.296 0.0
-61063 57.523 -68.368 130.659 -0.478 -0.827 0.296 0.0
-61064 57.715 -68.368 130.659 0.478 -0.827 0.296 0.0
-61065 57.763 -68.12 130.711 0.719 0.415 0.558 0.0
-61066 57.619 -68.037 130.711 0.0 0.83 0.558 0.0
-61067 57.475 -68.12 130.711 -0.719 0.415 0.558 0.0
-61068 57.475 -68.286 130.711 -0.719 -0.415 0.558 0.0
-61069 57.619 -68.369 130.711 0.0 -0.83 0.558 0.0
-61070 57.763 -68.286 130.711 0.719 -0.415 0.558 0.0
-1971 110 10 31
-61071 57.619 -68.203 121.7 -0.0 -0.0 -1.0 0.0
-61072 57.711 -68.15 121.731 0.46 0.266 -0.847 0.0
-61073 57.619 -68.097 121.731 0.0 0.531 -0.847 0.0
-61074 57.527 -68.15 121.731 -0.46 0.266 -0.847 0.0
-61075 57.527 -68.256 121.731 -0.46 -0.266 -0.847 0.0
-61076 57.619 -68.309 121.731 0.0 -0.531 -0.847 0.0
-61077 57.711 -68.256 121.731 0.46 -0.266 -0.847 0.0
-61078 57.785 -68.203 121.789 0.831 0.0 -0.556 0.0
-61079 57.702 -68.059 121.789 0.415 0.72 -0.556 0.0
-61080 57.536 -68.059 121.789 -0.415 0.72 -0.556 0.0
-61081 57.453 -68.203 121.789 -0.831 -0.0 -0.556 0.0
-61082 57.536 -68.347 121.789 -0.415 -0.72 -0.556 0.0
-61083 57.702 -68.347 121.789 0.415 -0.72 -0.556 0.0
-61084 57.784 -68.107 121.841 0.827 0.478 -0.296 0.0
-61085 57.619 -68.012 121.841 0.0 0.955 -0.296 0.0
-61086 57.454 -68.107 121.841 -0.827 0.478 -0.296 0.0
-61087 57.454 -68.299 121.841 -0.827 -0.478 -0.296 0.0
-61088 57.619 -68.394 121.841 0.0 -0.955 -0.296 0.0
-61089 57.784 -68.299 121.841 0.827 -0.478 -0.296 0.0
-61090 57.81 -68.203 121.959 0.955 0.0 0.296 0.0
-61091 57.715 -68.038 121.959 0.478 0.827 0.296 0.0
-61092 57.523 -68.038 121.959 -0.478 0.827 0.296 0.0
-61093 57.428 -68.203 121.959 -0.955 -0.0 0.296 0.0
-61094 57.523 -68.368 121.959 -0.478 -0.827 0.296 0.0
-61095 57.715 -68.368 121.959 0.478 -0.827 0.296 0.0
-61096 57.763 -68.12 122.011 0.719 0.415 0.558 0.0
-61097 57.619 -68.037 122.011 0.0 0.83 0.558 0.0
-61098 57.475 -68.12 122.011 -0.719 0.415 0.558 0.0
-61099 57.475 -68.286 122.011 -0.719 -0.415 0.558 0.0
-61100 57.619 -68.369 122.011 0.0 -0.83 0.558 0.0
-61101 57.763 -68.286 122.011 0.719 -0.415 0.558 0.0
-1972 110 9 31
-61102 57.619 -68.203 112.3 -0.0 -0.0 -1.0 0.0
-61103 57.711 -68.15 112.331 0.46 0.266 -0.847 0.0
-61104 57.619 -68.097 112.331 0.0 0.531 -0.847 0.0
-61105 57.527 -68.15 112.331 -0.46 0.266 -0.847 0.0
-61106 57.527 -68.256 112.331 -0.46 -0.266 -0.847 0.0
-61107 57.619 -68.309 112.331 0.0 -0.531 -0.847 0.0
-61108 57.711 -68.256 112.331 0.46 -0.266 -0.847 0.0
-61109 57.785 -68.203 112.389 0.831 0.0 -0.556 0.0
-61110 57.702 -68.059 112.389 0.415 0.72 -0.556 0.0
-61111 57.536 -68.059 112.389 -0.415 0.72 -0.556 0.0
-61112 57.453 -68.203 112.389 -0.831 -0.0 -0.556 0.0
-61113 57.536 -68.347 112.389 -0.415 -0.72 -0.556 0.0
-61114 57.702 -68.347 112.389 0.415 -0.72 -0.556 0.0
-61115 57.784 -68.107 112.441 0.827 0.478 -0.296 0.0
-61116 57.619 -68.012 112.441 0.0 0.955 -0.296 0.0
-61117 57.454 -68.107 112.441 -0.827 0.478 -0.296 0.0
-61118 57.454 -68.299 112.441 -0.827 -0.478 -0.296 0.0
-61119 57.619 -68.394 112.441 0.0 -0.955 -0.296 0.0
-61120 57.784 -68.299 112.441 0.827 -0.478 -0.296 0.0
-61121 57.81 -68.203 112.559 0.955 0.0 0.296 0.0
-61122 57.715 -68.038 112.559 0.478 0.827 0.296 0.0
-61123 57.523 -68.038 112.559 -0.478 0.827 0.296 0.0
-61124 57.428 -68.203 112.559 -0.955 -0.0 0.296 0.0
-61125 57.523 -68.368 112.559 -0.478 -0.827 0.296 0.0
-61126 57.715 -68.368 112.559 0.478 -0.827 0.296 0.0
-61127 57.763 -68.12 112.611 0.719 0.415 0.558 0.0
-61128 57.619 -68.037 112.611 0.0 0.83 0.558 0.0
-61129 57.475 -68.12 112.611 -0.719 0.415 0.558 0.0
-61130 57.475 -68.286 112.611 -0.719 -0.415 0.558 0.0
-61131 57.619 -68.369 112.611 0.0 -0.83 0.558 0.0
-61132 57.763 -68.286 112.611 0.719 -0.415 0.558 0.0
-1973 110 8 31
-61133 57.619 -68.203 103.6 -0.0 -0.0 -1.0 0.0
-61134 57.711 -68.15 103.631 0.46 0.266 -0.847 0.0
-61135 57.619 -68.097 103.631 0.0 0.531 -0.847 0.0
-61136 57.527 -68.15 103.631 -0.46 0.266 -0.847 0.0
-61137 57.527 -68.256 103.631 -0.46 -0.266 -0.847 0.0
-61138 57.619 -68.309 103.631 0.0 -0.531 -0.847 0.0
-61139 57.711 -68.256 103.631 0.46 -0.266 -0.847 0.0
-61140 57.785 -68.203 103.689 0.831 0.0 -0.556 0.0
-61141 57.702 -68.059 103.689 0.415 0.72 -0.556 0.0
-61142 57.536 -68.059 103.689 -0.415 0.72 -0.556 0.0
-61143 57.453 -68.203 103.689 -0.831 -0.0 -0.556 0.0
-61144 57.536 -68.347 103.689 -0.415 -0.72 -0.556 0.0
-61145 57.702 -68.347 103.689 0.415 -0.72 -0.556 0.0
-61146 57.784 -68.107 103.741 0.827 0.478 -0.296 0.0
-61147 57.619 -68.012 103.741 0.0 0.955 -0.296 0.0
-61148 57.454 -68.107 103.741 -0.827 0.478 -0.296 0.0
-61149 57.454 -68.299 103.741 -0.827 -0.478 -0.296 0.0
-61150 57.619 -68.394 103.741 0.0 -0.955 -0.296 0.0
-61151 57.784 -68.299 103.741 0.827 -0.478 -0.296 0.0
-61152 57.81 -68.203 103.859 0.955 0.0 0.296 0.0
-61153 57.715 -68.038 103.859 0.478 0.827 0.296 0.0
-61154 57.523 -68.038 103.859 -0.478 0.827 0.296 0.0
-61155 57.428 -68.203 103.859 -0.955 -0.0 0.296 0.0
-61156 57.523 -68.368 103.859 -0.478 -0.827 0.296 0.0
-61157 57.715 -68.368 103.859 0.478 -0.827 0.296 0.0
-61158 57.763 -68.12 103.911 0.719 0.415 0.558 0.0
-61159 57.619 -68.037 103.911 0.0 0.83 0.558 0.0
-61160 57.475 -68.12 103.911 -0.719 0.415 0.558 0.0
-61161 57.475 -68.286 103.911 -0.719 -0.415 0.558 0.0
-61162 57.619 -68.369 103.911 0.0 -0.83 0.558 0.0
-61163 57.763 -68.286 103.911 0.719 -0.415 0.558 0.0
-1974 110 7 31
-61164 57.619 -68.203 94.2 -0.0 -0.0 -1.0 0.0
-61165 57.711 -68.15 94.231 0.46 0.266 -0.847 0.0
-61166 57.619 -68.097 94.231 0.0 0.531 -0.847 0.0
-61167 57.527 -68.15 94.231 -0.46 0.266 -0.847 0.0
-61168 57.527 -68.256 94.231 -0.46 -0.266 -0.847 0.0
-61169 57.619 -68.309 94.231 0.0 -0.531 -0.847 0.0
-61170 57.711 -68.256 94.231 0.46 -0.266 -0.847 0.0
-61171 57.785 -68.203 94.289 0.831 0.0 -0.556 0.0
-61172 57.702 -68.059 94.289 0.415 0.72 -0.556 0.0
-61173 57.536 -68.059 94.289 -0.415 0.72 -0.556 0.0
-61174 57.453 -68.203 94.289 -0.831 -0.0 -0.556 0.0
-61175 57.536 -68.347 94.289 -0.415 -0.72 -0.556 0.0
-61176 57.702 -68.347 94.289 0.415 -0.72 -0.556 0.0
-61177 57.784 -68.107 94.341 0.827 0.478 -0.296 0.0
-61178 57.619 -68.012 94.341 0.0 0.955 -0.296 0.0
-61179 57.454 -68.107 94.341 -0.827 0.478 -0.296 0.0
-61180 57.454 -68.299 94.341 -0.827 -0.478 -0.296 0.0
-61181 57.619 -68.394 94.341 0.0 -0.955 -0.296 0.0
-61182 57.784 -68.299 94.341 0.827 -0.478 -0.296 0.0
-61183 57.81 -68.203 94.459 0.955 0.0 0.296 0.0
-61184 57.715 -68.038 94.459 0.478 0.827 0.296 0.0
-61185 57.523 -68.038 94.459 -0.478 0.827 0.296 0.0
-61186 57.428 -68.203 94.459 -0.955 -0.0 0.296 0.0
-61187 57.523 -68.368 94.459 -0.478 -0.827 0.296 0.0
-61188 57.715 -68.368 94.459 0.478 -0.827 0.296 0.0
-61189 57.763 -68.12 94.511 0.719 0.415 0.558 0.0
-61190 57.619 -68.037 94.511 0.0 0.83 0.558 0.0
-61191 57.475 -68.12 94.511 -0.719 0.415 0.558 0.0
-61192 57.475 -68.286 94.511 -0.719 -0.415 0.558 0.0
-61193 57.619 -68.369 94.511 0.0 -0.83 0.558 0.0
-61194 57.763 -68.286 94.511 0.719 -0.415 0.558 0.0
-1975 110 6 31
-61195 57.619 -68.203 83.3 -0.0 -0.0 -1.0 0.0
-61196 57.711 -68.15 83.331 0.46 0.266 -0.847 0.0
-61197 57.619 -68.097 83.331 0.0 0.531 -0.847 0.0
-61198 57.527 -68.15 83.331 -0.46 0.266 -0.847 0.0
-61199 57.527 -68.256 83.331 -0.46 -0.266 -0.847 0.0
-61200 57.619 -68.309 83.331 0.0 -0.531 -0.847 0.0
-61201 57.711 -68.256 83.331 0.46 -0.266 -0.847 0.0
-61202 57.785 -68.203 83.389 0.831 0.0 -0.556 0.0
-61203 57.702 -68.059 83.389 0.415 0.72 -0.556 0.0
-61204 57.536 -68.059 83.389 -0.415 0.72 -0.556 0.0
-61205 57.453 -68.203 83.389 -0.831 -0.0 -0.556 0.0
-61206 57.536 -68.347 83.389 -0.415 -0.72 -0.556 0.0
-61207 57.702 -68.347 83.389 0.415 -0.72 -0.556 0.0
-61208 57.784 -68.107 83.441 0.827 0.478 -0.296 0.0
-61209 57.619 -68.012 83.441 0.0 0.955 -0.296 0.0
-61210 57.454 -68.107 83.441 -0.827 0.478 -0.296 0.0
-61211 57.454 -68.299 83.441 -0.827 -0.478 -0.296 0.0
-61212 57.619 -68.394 83.441 0.0 -0.955 -0.296 0.0
-61213 57.784 -68.299 83.441 0.827 -0.478 -0.296 0.0
-61214 57.81 -68.203 83.559 0.955 0.0 0.296 0.0
-61215 57.715 -68.038 83.559 0.478 0.827 0.296 0.0
-61216 57.523 -68.038 83.559 -0.478 0.827 0.296 0.0
-61217 57.428 -68.203 83.559 -0.955 -0.0 0.296 0.0
-61218 57.523 -68.368 83.559 -0.478 -0.827 0.296 0.0
-61219 57.715 -68.368 83.559 0.478 -0.827 0.296 0.0
-61220 57.763 -68.12 83.611 0.719 0.415 0.558 0.0
-61221 57.619 -68.037 83.611 0.0 0.83 0.558 0.0
-61222 57.475 -68.12 83.611 -0.719 0.415 0.558 0.0
-61223 57.475 -68.286 83.611 -0.719 -0.415 0.558 0.0
-61224 57.619 -68.369 83.611 0.0 -0.83 0.558 0.0
-61225 57.763 -68.286 83.611 0.719 -0.415 0.558 0.0
-1976 110 5 31
-61226 57.619 -68.203 73.9 -0.0 -0.0 -1.0 0.0
-61227 57.711 -68.15 73.931 0.46 0.266 -0.847 0.0
-61228 57.619 -68.097 73.931 0.0 0.531 -0.847 0.0
-61229 57.527 -68.15 73.931 -0.46 0.266 -0.847 0.0
-61230 57.527 -68.256 73.931 -0.46 -0.266 -0.847 0.0
-61231 57.619 -68.309 73.931 0.0 -0.531 -0.847 0.0
-61232 57.711 -68.256 73.931 0.46 -0.266 -0.847 0.0
-61233 57.785 -68.203 73.989 0.831 0.0 -0.556 0.0
-61234 57.702 -68.059 73.989 0.415 0.72 -0.556 0.0
-61235 57.536 -68.059 73.989 -0.415 0.72 -0.556 0.0
-61236 57.453 -68.203 73.989 -0.831 -0.0 -0.556 0.0
-61237 57.536 -68.347 73.989 -0.415 -0.72 -0.556 0.0
-61238 57.702 -68.347 73.989 0.415 -0.72 -0.556 0.0
-61239 57.784 -68.107 74.041 0.827 0.478 -0.296 0.0
-61240 57.619 -68.012 74.041 0.0 0.955 -0.296 0.0
-61241 57.454 -68.107 74.041 -0.827 0.478 -0.296 0.0
-61242 57.454 -68.299 74.041 -0.827 -0.478 -0.296 0.0
-61243 57.619 -68.394 74.041 0.0 -0.955 -0.296 0.0
-61244 57.784 -68.299 74.041 0.827 -0.478 -0.296 0.0
-61245 57.81 -68.203 74.159 0.955 0.0 0.296 0.0
-61246 57.715 -68.038 74.159 0.478 0.827 0.296 0.0
-61247 57.523 -68.038 74.159 -0.478 0.827 0.296 0.0
-61248 57.428 -68.203 74.159 -0.955 -0.0 0.296 0.0
-61249 57.523 -68.368 74.159 -0.478 -0.827 0.296 0.0
-61250 57.715 -68.368 74.159 0.478 -0.827 0.296 0.0
-61251 57.763 -68.12 74.211 0.719 0.415 0.558 0.0
-61252 57.619 -68.037 74.211 0.0 0.83 0.558 0.0
-61253 57.475 -68.12 74.211 -0.719 0.415 0.558 0.0
-61254 57.475 -68.286 74.211 -0.719 -0.415 0.558 0.0
-61255 57.619 -68.369 74.211 0.0 -0.83 0.558 0.0
-61256 57.763 -68.286 74.211 0.719 -0.415 0.558 0.0
-1977 110 4 31
-61257 57.619 -68.203 65.2 -0.0 -0.0 -1.0 0.0
-61258 57.711 -68.15 65.231 0.46 0.266 -0.847 0.0
-61259 57.619 -68.097 65.231 0.0 0.531 -0.847 0.0
-61260 57.527 -68.15 65.231 -0.46 0.266 -0.847 0.0
-61261 57.527 -68.256 65.231 -0.46 -0.266 -0.847 0.0
-61262 57.619 -68.309 65.231 0.0 -0.531 -0.847 0.0
-61263 57.711 -68.256 65.231 0.46 -0.266 -0.847 0.0
-61264 57.785 -68.203 65.289 0.831 0.0 -0.556 0.0
-61265 57.702 -68.059 65.289 0.415 0.72 -0.556 0.0
-61266 57.536 -68.059 65.289 -0.415 0.72 -0.556 0.0
-61267 57.453 -68.203 65.289 -0.831 -0.0 -0.556 0.0
-61268 57.536 -68.347 65.289 -0.415 -0.72 -0.556 0.0
-61269 57.702 -68.347 65.289 0.415 -0.72 -0.556 0.0
-61270 57.784 -68.107 65.341 0.827 0.478 -0.296 0.0
-61271 57.619 -68.012 65.341 0.0 0.955 -0.296 0.0
-61272 57.454 -68.107 65.341 -0.827 0.478 -0.296 0.0
-61273 57.454 -68.299 65.341 -0.827 -0.478 -0.296 0.0
-61274 57.619 -68.394 65.341 0.0 -0.955 -0.296 0.0
-61275 57.784 -68.299 65.341 0.827 -0.478 -0.296 0.0
-61276 57.81 -68.203 65.459 0.955 0.0 0.296 0.0
-61277 57.715 -68.038 65.459 0.478 0.827 0.296 0.0
-61278 57.523 -68.038 65.459 -0.478 0.827 0.296 0.0
-61279 57.428 -68.203 65.459 -0.955 -0.0 0.296 0.0
-61280 57.523 -68.368 65.459 -0.478 -0.827 0.296 0.0
-61281 57.715 -68.368 65.459 0.478 -0.827 0.296 0.0
-61282 57.763 -68.12 65.511 0.719 0.415 0.558 0.0
-61283 57.619 -68.037 65.511 0.0 0.83 0.558 0.0
-61284 57.475 -68.12 65.511 -0.719 0.415 0.558 0.0
-61285 57.475 -68.286 65.511 -0.719 -0.415 0.558 0.0
-61286 57.619 -68.369 65.511 0.0 -0.83 0.558 0.0
-61287 57.763 -68.286 65.511 0.719 -0.415 0.558 0.0
-1978 110 3 31
-61288 57.619 -68.203 55.8 -0.0 -0.0 -1.0 0.0
-61289 57.711 -68.15 55.831 0.46 0.266 -0.847 0.0
-61290 57.619 -68.097 55.831 0.0 0.531 -0.847 0.0
-61291 57.527 -68.15 55.831 -0.46 0.266 -0.847 0.0
-61292 57.527 -68.256 55.831 -0.46 -0.266 -0.847 0.0
-61293 57.619 -68.309 55.831 0.0 -0.531 -0.847 0.0
-61294 57.711 -68.256 55.831 0.46 -0.266 -0.847 0.0
-61295 57.785 -68.203 55.889 0.831 0.0 -0.556 0.0
-61296 57.702 -68.059 55.889 0.415 0.72 -0.556 0.0
-61297 57.536 -68.059 55.889 -0.415 0.72 -0.556 0.0
-61298 57.453 -68.203 55.889 -0.831 -0.0 -0.556 0.0
-61299 57.536 -68.347 55.889 -0.415 -0.72 -0.556 0.0
-61300 57.702 -68.347 55.889 0.415 -0.72 -0.556 0.0
-61301 57.784 -68.107 55.941 0.827 0.478 -0.296 0.0
-61302 57.619 -68.012 55.941 0.0 0.955 -0.296 0.0
-61303 57.454 -68.107 55.941 -0.827 0.478 -0.296 0.0
-61304 57.454 -68.299 55.941 -0.827 -0.478 -0.296 0.0
-61305 57.619 -68.394 55.941 0.0 -0.955 -0.296 0.0
-61306 57.784 -68.299 55.941 0.827 -0.478 -0.296 0.0
-61307 57.81 -68.203 56.059 0.955 0.0 0.296 0.0
-61308 57.715 -68.038 56.059 0.478 0.827 0.296 0.0
-61309 57.523 -68.038 56.059 -0.478 0.827 0.296 0.0
-61310 57.428 -68.203 56.059 -0.955 -0.0 0.296 0.0
-61311 57.523 -68.368 56.059 -0.478 -0.827 0.296 0.0
-61312 57.715 -68.368 56.059 0.478 -0.827 0.296 0.0
-61313 57.763 -68.12 56.111 0.719 0.415 0.558 0.0
-61314 57.619 -68.037 56.111 0.0 0.83 0.558 0.0
-61315 57.475 -68.12 56.111 -0.719 0.415 0.558 0.0
-61316 57.475 -68.286 56.111 -0.719 -0.415 0.558 0.0
-61317 57.619 -68.369 56.111 0.0 -0.83 0.558 0.0
-61318 57.763 -68.286 56.111 0.719 -0.415 0.558 0.0
-1979 110 2 31
-61319 57.619 -68.203 47.1 -0.0 -0.0 -1.0 0.0
-61320 57.711 -68.15 47.131 0.46 0.266 -0.847 0.0
-61321 57.619 -68.097 47.131 0.0 0.531 -0.847 0.0
-61322 57.527 -68.15 47.131 -0.46 0.266 -0.847 0.0
-61323 57.527 -68.256 47.131 -0.46 -0.266 -0.847 0.0
-61324 57.619 -68.309 47.131 0.0 -0.531 -0.847 0.0
-61325 57.711 -68.256 47.131 0.46 -0.266 -0.847 0.0
-61326 57.785 -68.203 47.189 0.831 0.0 -0.556 0.0
-61327 57.702 -68.059 47.189 0.415 0.72 -0.556 0.0
-61328 57.536 -68.059 47.189 -0.415 0.72 -0.556 0.0
-61329 57.453 -68.203 47.189 -0.831 -0.0 -0.556 0.0
-61330 57.536 -68.347 47.189 -0.415 -0.72 -0.556 0.0
-61331 57.702 -68.347 47.189 0.415 -0.72 -0.556 0.0
-61332 57.784 -68.107 47.241 0.827 0.478 -0.296 0.0
-61333 57.619 -68.012 47.241 0.0 0.955 -0.296 0.0
-61334 57.454 -68.107 47.241 -0.827 0.478 -0.296 0.0
-61335 57.454 -68.299 47.241 -0.827 -0.478 -0.296 0.0
-61336 57.619 -68.394 47.241 0.0 -0.955 -0.296 0.0
-61337 57.784 -68.299 47.241 0.827 -0.478 -0.296 0.0
-61338 57.81 -68.203 47.359 0.955 0.0 0.296 0.0
-61339 57.715 -68.038 47.359 0.478 0.827 0.296 0.0
-61340 57.523 -68.038 47.359 -0.478 0.827 0.296 0.0
-61341 57.428 -68.203 47.359 -0.955 -0.0 0.296 0.0
-61342 57.523 -68.368 47.359 -0.478 -0.827 0.296 0.0
-61343 57.715 -68.368 47.359 0.478 -0.827 0.296 0.0
-61344 57.763 -68.12 47.411 0.719 0.415 0.558 0.0
-61345 57.619 -68.037 47.411 0.0 0.83 0.558 0.0
-61346 57.475 -68.12 47.411 -0.719 0.415 0.558 0.0
-61347 57.475 -68.286 47.411 -0.719 -0.415 0.558 0.0
-61348 57.619 -68.369 47.411 0.0 -0.83 0.558 0.0
-61349 57.763 -68.286 47.411 0.719 -0.415 0.558 0.0
-1980 110 1 31
-61350 57.619 -68.203 37.7 -0.0 -0.0 -1.0 0.0
-61351 57.711 -68.15 37.731 0.46 0.266 -0.847 0.0
-61352 57.619 -68.097 37.731 0.0 0.531 -0.847 0.0
-61353 57.527 -68.15 37.731 -0.46 0.266 -0.847 0.0
-61354 57.527 -68.256 37.731 -0.46 -0.266 -0.847 0.0
-61355 57.619 -68.309 37.731 0.0 -0.531 -0.847 0.0
-61356 57.711 -68.256 37.731 0.46 -0.266 -0.847 0.0
-61357 57.785 -68.203 37.789 0.831 0.0 -0.556 0.0
-61358 57.702 -68.059 37.789 0.415 0.72 -0.556 0.0
-61359 57.536 -68.059 37.789 -0.415 0.72 -0.556 0.0
-61360 57.453 -68.203 37.789 -0.831 -0.0 -0.556 0.0
-61361 57.536 -68.347 37.789 -0.415 -0.72 -0.556 0.0
-61362 57.702 -68.347 37.789 0.415 -0.72 -0.556 0.0
-61363 57.784 -68.107 37.841 0.827 0.478 -0.296 0.0
-61364 57.619 -68.012 37.841 0.0 0.955 -0.296 0.0
-61365 57.454 -68.107 37.841 -0.827 0.478 -0.296 0.0
-61366 57.454 -68.299 37.841 -0.827 -0.478 -0.296 0.0
-61367 57.619 -68.394 37.841 0.0 -0.955 -0.296 0.0
-61368 57.784 -68.299 37.841 0.827 -0.478 -0.296 0.0
-61369 57.81 -68.203 37.959 0.955 0.0 0.296 0.0
-61370 57.715 -68.038 37.959 0.478 0.827 0.296 0.0
-61371 57.523 -68.038 37.959 -0.478 0.827 0.296 0.0
-61372 57.428 -68.203 37.959 -0.955 -0.0 0.296 0.0
-61373 57.523 -68.368 37.959 -0.478 -0.827 0.296 0.0
-61374 57.715 -68.368 37.959 0.478 -0.827 0.296 0.0
-61375 57.763 -68.12 38.011 0.719 0.415 0.558 0.0
-61376 57.619 -68.037 38.011 0.0 0.83 0.558 0.0
-61377 57.475 -68.12 38.011 -0.719 0.415 0.558 0.0
-61378 57.475 -68.286 38.011 -0.719 -0.415 0.558 0.0
-61379 57.619 -68.369 38.011 0.0 -0.83 0.558 0.0
-61380 57.763 -68.286 38.011 0.719 -0.415 0.558 0.0
-1981 111 18 31
-61381 80.677 -68.791 196.3 -0.0 -0.0 -1.0 0.0
-61382 80.769 -68.738 196.331 0.46 0.266 -0.847 0.0
-61383 80.677 -68.685 196.331 0.0 0.531 -0.847 0.0
-61384 80.585 -68.738 196.331 -0.46 0.266 -0.847 0.0
-61385 80.585 -68.844 196.331 -0.46 -0.266 -0.847 0.0
-61386 80.677 -68.897 196.331 0.0 -0.531 -0.847 0.0
-61387 80.769 -68.844 196.331 0.46 -0.266 -0.847 0.0
-61388 80.843 -68.791 196.389 0.831 0.0 -0.556 0.0
-61389 80.76 -68.647 196.389 0.415 0.72 -0.556 0.0
-61390 80.594 -68.647 196.389 -0.415 0.72 -0.556 0.0
-61391 80.511 -68.791 196.389 -0.831 -0.0 -0.556 0.0
-61392 80.594 -68.935 196.389 -0.415 -0.72 -0.556 0.0
-61393 80.76 -68.935 196.389 0.415 -0.72 -0.556 0.0
-61394 80.842 -68.695 196.441 0.827 0.478 -0.296 0.0
-61395 80.677 -68.6 196.441 0.0 0.955 -0.296 0.0
-61396 80.512 -68.695 196.441 -0.827 0.478 -0.296 0.0
-61397 80.512 -68.887 196.441 -0.827 -0.478 -0.296 0.0
-61398 80.677 -68.982 196.441 0.0 -0.955 -0.296 0.0
-61399 80.842 -68.887 196.441 0.827 -0.478 -0.296 0.0
-61400 80.868 -68.791 196.559 0.955 0.0 0.296 0.0
-61401 80.773 -68.626 196.559 0.478 0.827 0.296 0.0
-61402 80.581 -68.626 196.559 -0.478 0.827 0.296 0.0
-61403 80.486 -68.791 196.559 -0.955 -0.0 0.296 0.0
-61404 80.581 -68.956 196.559 -0.478 -0.827 0.296 0.0
-61405 80.773 -68.956 196.559 0.478 -0.827 0.296 0.0
-61406 80.821 -68.708 196.611 0.719 0.415 0.558 0.0
-61407 80.677 -68.625 196.611 0.0 0.83 0.558 0.0
-61408 80.533 -68.708 196.611 -0.719 0.415 0.558 0.0
-61409 80.533 -68.874 196.611 -0.719 -0.415 0.558 0.0
-61410 80.677 -68.957 196.611 0.0 -0.83 0.558 0.0
-61411 80.821 -68.874 196.611 0.719 -0.415 0.558 0.0
-1982 111 17 31
-61412 80.677 -68.791 186.9 -0.0 -0.0 -1.0 0.0
-61413 80.769 -68.738 186.931 0.46 0.266 -0.847 0.0
-61414 80.677 -68.685 186.931 0.0 0.531 -0.847 0.0
-61415 80.585 -68.738 186.931 -0.46 0.266 -0.847 0.0
-61416 80.585 -68.844 186.931 -0.46 -0.266 -0.847 0.0
-61417 80.677 -68.897 186.931 0.0 -0.531 -0.847 0.0
-61418 80.769 -68.844 186.931 0.46 -0.266 -0.847 0.0
-61419 80.843 -68.791 186.989 0.831 0.0 -0.556 0.0
-61420 80.76 -68.647 186.989 0.415 0.72 -0.556 0.0
-61421 80.594 -68.647 186.989 -0.415 0.72 -0.556 0.0
-61422 80.511 -68.791 186.989 -0.831 -0.0 -0.556 0.0
-61423 80.594 -68.935 186.989 -0.415 -0.72 -0.556 0.0
-61424 80.76 -68.935 186.989 0.415 -0.72 -0.556 0.0
-61425 80.842 -68.695 187.041 0.827 0.478 -0.296 0.0
-61426 80.677 -68.6 187.041 0.0 0.955 -0.296 0.0
-61427 80.512 -68.695 187.041 -0.827 0.478 -0.296 0.0
-61428 80.512 -68.887 187.041 -0.827 -0.478 -0.296 0.0
-61429 80.677 -68.982 187.041 0.0 -0.955 -0.296 0.0
-61430 80.842 -68.887 187.041 0.827 -0.478 -0.296 0.0
-61431 80.868 -68.791 187.159 0.955 0.0 0.296 0.0
-61432 80.773 -68.626 187.159 0.478 0.827 0.296 0.0
-61433 80.581 -68.626 187.159 -0.478 0.827 0.296 0.0
-61434 80.486 -68.791 187.159 -0.955 -0.0 0.296 0.0
-61435 80.581 -68.956 187.159 -0.478 -0.827 0.296 0.0
-61436 80.773 -68.956 187.159 0.478 -0.827 0.296 0.0
-61437 80.821 -68.708 187.211 0.719 0.415 0.558 0.0
-61438 80.677 -68.625 187.211 0.0 0.83 0.558 0.0
-61439 80.533 -68.708 187.211 -0.719 0.415 0.558 0.0
-61440 80.533 -68.874 187.211 -0.719 -0.415 0.558 0.0
-61441 80.677 -68.957 187.211 0.0 -0.83 0.558 0.0
-61442 80.821 -68.874 187.211 0.719 -0.415 0.558 0.0
-1983 111 16 31
-61443 80.677 -68.791 178.2 -0.0 -0.0 -1.0 0.0
-61444 80.769 -68.738 178.231 0.46 0.266 -0.847 0.0
-61445 80.677 -68.685 178.231 0.0 0.531 -0.847 0.0
-61446 80.585 -68.738 178.231 -0.46 0.266 -0.847 0.0
-61447 80.585 -68.844 178.231 -0.46 -0.266 -0.847 0.0
-61448 80.677 -68.897 178.231 0.0 -0.531 -0.847 0.0
-61449 80.769 -68.844 178.231 0.46 -0.266 -0.847 0.0
-61450 80.843 -68.791 178.289 0.831 0.0 -0.556 0.0
-61451 80.76 -68.647 178.289 0.415 0.72 -0.556 0.0
-61452 80.594 -68.647 178.289 -0.415 0.72 -0.556 0.0
-61453 80.511 -68.791 178.289 -0.831 -0.0 -0.556 0.0
-61454 80.594 -68.935 178.289 -0.415 -0.72 -0.556 0.0
-61455 80.76 -68.935 178.289 0.415 -0.72 -0.556 0.0
-61456 80.842 -68.695 178.341 0.827 0.478 -0.296 0.0
-61457 80.677 -68.6 178.341 0.0 0.955 -0.296 0.0
-61458 80.512 -68.695 178.341 -0.827 0.478 -0.296 0.0
-61459 80.512 -68.887 178.341 -0.827 -0.478 -0.296 0.0
-61460 80.677 -68.982 178.341 0.0 -0.955 -0.296 0.0
-61461 80.842 -68.887 178.341 0.827 -0.478 -0.296 0.0
-61462 80.868 -68.791 178.459 0.955 0.0 0.296 0.0
-61463 80.773 -68.626 178.459 0.478 0.827 0.296 0.0
-61464 80.581 -68.626 178.459 -0.478 0.827 0.296 0.0
-61465 80.486 -68.791 178.459 -0.955 -0.0 0.296 0.0
-61466 80.581 -68.956 178.459 -0.478 -0.827 0.296 0.0
-61467 80.773 -68.956 178.459 0.478 -0.827 0.296 0.0
-61468 80.821 -68.708 178.511 0.719 0.415 0.558 0.0
-61469 80.677 -68.625 178.511 0.0 0.83 0.558 0.0
-61470 80.533 -68.708 178.511 -0.719 0.415 0.558 0.0
-61471 80.533 -68.874 178.511 -0.719 -0.415 0.558 0.0
-61472 80.677 -68.957 178.511 0.0 -0.83 0.558 0.0
-61473 80.821 -68.874 178.511 0.719 -0.415 0.558 0.0
-1984 111 15 31
-61474 80.677 -68.791 168.8 -0.0 -0.0 -1.0 0.0
-61475 80.769 -68.738 168.831 0.46 0.266 -0.847 0.0
-61476 80.677 -68.685 168.831 0.0 0.531 -0.847 0.0
-61477 80.585 -68.738 168.831 -0.46 0.266 -0.847 0.0
-61478 80.585 -68.844 168.831 -0.46 -0.266 -0.847 0.0
-61479 80.677 -68.897 168.831 0.0 -0.531 -0.847 0.0
-61480 80.769 -68.844 168.831 0.46 -0.266 -0.847 0.0
-61481 80.843 -68.791 168.889 0.831 0.0 -0.556 0.0
-61482 80.76 -68.647 168.889 0.415 0.72 -0.556 0.0
-61483 80.594 -68.647 168.889 -0.415 0.72 -0.556 0.0
-61484 80.511 -68.791 168.889 -0.831 -0.0 -0.556 0.0
-61485 80.594 -68.935 168.889 -0.415 -0.72 -0.556 0.0
-61486 80.76 -68.935 168.889 0.415 -0.72 -0.556 0.0
-61487 80.842 -68.695 168.941 0.827 0.478 -0.296 0.0
-61488 80.677 -68.6 168.941 0.0 0.955 -0.296 0.0
-61489 80.512 -68.695 168.941 -0.827 0.478 -0.296 0.0
-61490 80.512 -68.887 168.941 -0.827 -0.478 -0.296 0.0
-61491 80.677 -68.982 168.941 0.0 -0.955 -0.296 0.0
-61492 80.842 -68.887 168.941 0.827 -0.478 -0.296 0.0
-61493 80.868 -68.791 169.059 0.955 0.0 0.296 0.0
-61494 80.773 -68.626 169.059 0.478 0.827 0.296 0.0
-61495 80.581 -68.626 169.059 -0.478 0.827 0.296 0.0
-61496 80.486 -68.791 169.059 -0.955 -0.0 0.296 0.0
-61497 80.581 -68.956 169.059 -0.478 -0.827 0.296 0.0
-61498 80.773 -68.956 169.059 0.478 -0.827 0.296 0.0
-61499 80.821 -68.708 169.111 0.719 0.415 0.558 0.0
-61500 80.677 -68.625 169.111 0.0 0.83 0.558 0.0
-61501 80.533 -68.708 169.111 -0.719 0.415 0.558 0.0
-61502 80.533 -68.874 169.111 -0.719 -0.415 0.558 0.0
-61503 80.677 -68.957 169.111 0.0 -0.83 0.558 0.0
-61504 80.821 -68.874 169.111 0.719 -0.415 0.558 0.0
-1985 111 14 31
-61505 80.677 -68.791 160.1 -0.0 -0.0 -1.0 0.0
-61506 80.769 -68.738 160.131 0.46 0.266 -0.847 0.0
-61507 80.677 -68.685 160.131 0.0 0.531 -0.847 0.0
-61508 80.585 -68.738 160.131 -0.46 0.266 -0.847 0.0
-61509 80.585 -68.844 160.131 -0.46 -0.266 -0.847 0.0
-61510 80.677 -68.897 160.131 0.0 -0.531 -0.847 0.0
-61511 80.769 -68.844 160.131 0.46 -0.266 -0.847 0.0
-61512 80.843 -68.791 160.189 0.831 0.0 -0.556 0.0
-61513 80.76 -68.647 160.189 0.415 0.72 -0.556 0.0
-61514 80.594 -68.647 160.189 -0.415 0.72 -0.556 0.0
-61515 80.511 -68.791 160.189 -0.831 -0.0 -0.556 0.0
-61516 80.594 -68.935 160.189 -0.415 -0.72 -0.556 0.0
-61517 80.76 -68.935 160.189 0.415 -0.72 -0.556 0.0
-61518 80.842 -68.695 160.241 0.827 0.478 -0.296 0.0
-61519 80.677 -68.6 160.241 0.0 0.955 -0.296 0.0
-61520 80.512 -68.695 160.241 -0.827 0.478 -0.296 0.0
-61521 80.512 -68.887 160.241 -0.827 -0.478 -0.296 0.0
-61522 80.677 -68.982 160.241 0.0 -0.955 -0.296 0.0
-61523 80.842 -68.887 160.241 0.827 -0.478 -0.296 0.0
-61524 80.868 -68.791 160.359 0.955 0.0 0.296 0.0
-61525 80.773 -68.626 160.359 0.478 0.827 0.296 0.0
-61526 80.581 -68.626 160.359 -0.478 0.827 0.296 0.0
-61527 80.486 -68.791 160.359 -0.955 -0.0 0.296 0.0
-61528 80.581 -68.956 160.359 -0.478 -0.827 0.296 0.0
-61529 80.773 -68.956 160.359 0.478 -0.827 0.296 0.0
-61530 80.821 -68.708 160.411 0.719 0.415 0.558 0.0
-61531 80.677 -68.625 160.411 0.0 0.83 0.558 0.0
-61532 80.533 -68.708 160.411 -0.719 0.415 0.558 0.0
-61533 80.533 -68.874 160.411 -0.719 -0.415 0.558 0.0
-61534 80.677 -68.957 160.411 0.0 -0.83 0.558 0.0
-61535 80.821 -68.874 160.411 0.719 -0.415 0.558 0.0
-1986 111 13 31
-61536 80.677 -68.791 150.7 -0.0 -0.0 -1.0 0.0
-61537 80.769 -68.738 150.731 0.46 0.266 -0.847 0.0
-61538 80.677 -68.685 150.731 0.0 0.531 -0.847 0.0
-61539 80.585 -68.738 150.731 -0.46 0.266 -0.847 0.0
-61540 80.585 -68.844 150.731 -0.46 -0.266 -0.847 0.0
-61541 80.677 -68.897 150.731 0.0 -0.531 -0.847 0.0
-61542 80.769 -68.844 150.731 0.46 -0.266 -0.847 0.0
-61543 80.843 -68.791 150.789 0.831 0.0 -0.556 0.0
-61544 80.76 -68.647 150.789 0.415 0.72 -0.556 0.0
-61545 80.594 -68.647 150.789 -0.415 0.72 -0.556 0.0
-61546 80.511 -68.791 150.789 -0.831 -0.0 -0.556 0.0
-61547 80.594 -68.935 150.789 -0.415 -0.72 -0.556 0.0
-61548 80.76 -68.935 150.789 0.415 -0.72 -0.556 0.0
-61549 80.842 -68.695 150.841 0.827 0.478 -0.296 0.0
-61550 80.677 -68.6 150.841 0.0 0.955 -0.296 0.0
-61551 80.512 -68.695 150.841 -0.827 0.478 -0.296 0.0
-61552 80.512 -68.887 150.841 -0.827 -0.478 -0.296 0.0
-61553 80.677 -68.982 150.841 0.0 -0.955 -0.296 0.0
-61554 80.842 -68.887 150.841 0.827 -0.478 -0.296 0.0
-61555 80.868 -68.791 150.959 0.955 0.0 0.296 0.0
-61556 80.773 -68.626 150.959 0.478 0.827 0.296 0.0
-61557 80.581 -68.626 150.959 -0.478 0.827 0.296 0.0
-61558 80.486 -68.791 150.959 -0.955 -0.0 0.296 0.0
-61559 80.581 -68.956 150.959 -0.478 -0.827 0.296 0.0
-61560 80.773 -68.956 150.959 0.478 -0.827 0.296 0.0
-61561 80.821 -68.708 151.011 0.719 0.415 0.558 0.0
-61562 80.677 -68.625 151.011 0.0 0.83 0.558 0.0
-61563 80.533 -68.708 151.011 -0.719 0.415 0.558 0.0
-61564 80.533 -68.874 151.011 -0.719 -0.415 0.558 0.0
-61565 80.677 -68.957 151.011 0.0 -0.83 0.558 0.0
-61566 80.821 -68.874 151.011 0.719 -0.415 0.558 0.0
-1987 111 12 31
-61567 80.677 -68.791 139.8 -0.0 -0.0 -1.0 0.0
-61568 80.769 -68.738 139.831 0.46 0.266 -0.847 0.0
-61569 80.677 -68.685 139.831 0.0 0.531 -0.847 0.0
-61570 80.585 -68.738 139.831 -0.46 0.266 -0.847 0.0
-61571 80.585 -68.844 139.831 -0.46 -0.266 -0.847 0.0
-61572 80.677 -68.897 139.831 0.0 -0.531 -0.847 0.0
-61573 80.769 -68.844 139.831 0.46 -0.266 -0.847 0.0
-61574 80.843 -68.791 139.889 0.831 0.0 -0.556 0.0
-61575 80.76 -68.647 139.889 0.415 0.72 -0.556 0.0
-61576 80.594 -68.647 139.889 -0.415 0.72 -0.556 0.0
-61577 80.511 -68.791 139.889 -0.831 -0.0 -0.556 0.0
-61578 80.594 -68.935 139.889 -0.415 -0.72 -0.556 0.0
-61579 80.76 -68.935 139.889 0.415 -0.72 -0.556 0.0
-61580 80.842 -68.695 139.941 0.827 0.478 -0.296 0.0
-61581 80.677 -68.6 139.941 0.0 0.955 -0.296 0.0
-61582 80.512 -68.695 139.941 -0.827 0.478 -0.296 0.0
-61583 80.512 -68.887 139.941 -0.827 -0.478 -0.296 0.0
-61584 80.677 -68.982 139.941 0.0 -0.955 -0.296 0.0
-61585 80.842 -68.887 139.941 0.827 -0.478 -0.296 0.0
-61586 80.868 -68.791 140.059 0.955 0.0 0.296 0.0
-61587 80.773 -68.626 140.059 0.478 0.827 0.296 0.0
-61588 80.581 -68.626 140.059 -0.478 0.827 0.296 0.0
-61589 80.486 -68.791 140.059 -0.955 -0.0 0.296 0.0
-61590 80.581 -68.956 140.059 -0.478 -0.827 0.296 0.0
-61591 80.773 -68.956 140.059 0.478 -0.827 0.296 0.0
-61592 80.821 -68.708 140.111 0.719 0.415 0.558 0.0
-61593 80.677 -68.625 140.111 0.0 0.83 0.558 0.0
-61594 80.533 -68.708 140.111 -0.719 0.415 0.558 0.0
-61595 80.533 -68.874 140.111 -0.719 -0.415 0.558 0.0
-61596 80.677 -68.957 140.111 0.0 -0.83 0.558 0.0
-61597 80.821 -68.874 140.111 0.719 -0.415 0.558 0.0
-1988 111 11 31
-61598 80.677 -68.791 130.4 -0.0 -0.0 -1.0 0.0
-61599 80.769 -68.738 130.431 0.46 0.266 -0.847 0.0
-61600 80.677 -68.685 130.431 0.0 0.531 -0.847 0.0
-61601 80.585 -68.738 130.431 -0.46 0.266 -0.847 0.0
-61602 80.585 -68.844 130.431 -0.46 -0.266 -0.847 0.0
-61603 80.677 -68.897 130.431 0.0 -0.531 -0.847 0.0
-61604 80.769 -68.844 130.431 0.46 -0.266 -0.847 0.0
-61605 80.843 -68.791 130.489 0.831 0.0 -0.556 0.0
-61606 80.76 -68.647 130.489 0.415 0.72 -0.556 0.0
-61607 80.594 -68.647 130.489 -0.415 0.72 -0.556 0.0
-61608 80.511 -68.791 130.489 -0.831 -0.0 -0.556 0.0
-61609 80.594 -68.935 130.489 -0.415 -0.72 -0.556 0.0
-61610 80.76 -68.935 130.489 0.415 -0.72 -0.556 0.0
-61611 80.842 -68.695 130.541 0.827 0.478 -0.296 0.0
-61612 80.677 -68.6 130.541 0.0 0.955 -0.296 0.0
-61613 80.512 -68.695 130.541 -0.827 0.478 -0.296 0.0
-61614 80.512 -68.887 130.541 -0.827 -0.478 -0.296 0.0
-61615 80.677 -68.982 130.541 0.0 -0.955 -0.296 0.0
-61616 80.842 -68.887 130.541 0.827 -0.478 -0.296 0.0
-61617 80.868 -68.791 130.659 0.955 0.0 0.296 0.0
-61618 80.773 -68.626 130.659 0.478 0.827 0.296 0.0
-61619 80.581 -68.626 130.659 -0.478 0.827 0.296 0.0
-61620 80.486 -68.791 130.659 -0.955 -0.0 0.296 0.0
-61621 80.581 -68.956 130.659 -0.478 -0.827 0.296 0.0
-61622 80.773 -68.956 130.659 0.478 -0.827 0.296 0.0
-61623 80.821 -68.708 130.711 0.719 0.415 0.558 0.0
-61624 80.677 -68.625 130.711 0.0 0.83 0.558 0.0
-61625 80.533 -68.708 130.711 -0.719 0.415 0.558 0.0
-61626 80.533 -68.874 130.711 -0.719 -0.415 0.558 0.0
-61627 80.677 -68.957 130.711 0.0 -0.83 0.558 0.0
-61628 80.821 -68.874 130.711 0.719 -0.415 0.558 0.0
-1989 111 10 31
-61629 80.677 -68.791 121.7 -0.0 -0.0 -1.0 0.0
-61630 80.769 -68.738 121.731 0.46 0.266 -0.847 0.0
-61631 80.677 -68.685 121.731 0.0 0.531 -0.847 0.0
-61632 80.585 -68.738 121.731 -0.46 0.266 -0.847 0.0
-61633 80.585 -68.844 121.731 -0.46 -0.266 -0.847 0.0
-61634 80.677 -68.897 121.731 0.0 -0.531 -0.847 0.0
-61635 80.769 -68.844 121.731 0.46 -0.266 -0.847 0.0
-61636 80.843 -68.791 121.789 0.831 0.0 -0.556 0.0
-61637 80.76 -68.647 121.789 0.415 0.72 -0.556 0.0
-61638 80.594 -68.647 121.789 -0.415 0.72 -0.556 0.0
-61639 80.511 -68.791 121.789 -0.831 -0.0 -0.556 0.0
-61640 80.594 -68.935 121.789 -0.415 -0.72 -0.556 0.0
-61641 80.76 -68.935 121.789 0.415 -0.72 -0.556 0.0
-61642 80.842 -68.695 121.841 0.827 0.478 -0.296 0.0
-61643 80.677 -68.6 121.841 0.0 0.955 -0.296 0.0
-61644 80.512 -68.695 121.841 -0.827 0.478 -0.296 0.0
-61645 80.512 -68.887 121.841 -0.827 -0.478 -0.296 0.0
-61646 80.677 -68.982 121.841 0.0 -0.955 -0.296 0.0
-61647 80.842 -68.887 121.841 0.827 -0.478 -0.296 0.0
-61648 80.868 -68.791 121.959 0.955 0.0 0.296 0.0
-61649 80.773 -68.626 121.959 0.478 0.827 0.296 0.0
-61650 80.581 -68.626 121.959 -0.478 0.827 0.296 0.0
-61651 80.486 -68.791 121.959 -0.955 -0.0 0.296 0.0
-61652 80.581 -68.956 121.959 -0.478 -0.827 0.296 0.0
-61653 80.773 -68.956 121.959 0.478 -0.827 0.296 0.0
-61654 80.821 -68.708 122.011 0.719 0.415 0.558 0.0
-61655 80.677 -68.625 122.011 0.0 0.83 0.558 0.0
-61656 80.533 -68.708 122.011 -0.719 0.415 0.558 0.0
-61657 80.533 -68.874 122.011 -0.719 -0.415 0.558 0.0
-61658 80.677 -68.957 122.011 0.0 -0.83 0.558 0.0
-61659 80.821 -68.874 122.011 0.719 -0.415 0.558 0.0
-1990 111 9 31
-61660 80.677 -68.791 112.3 -0.0 -0.0 -1.0 0.0
-61661 80.769 -68.738 112.331 0.46 0.266 -0.847 0.0
-61662 80.677 -68.685 112.331 0.0 0.531 -0.847 0.0
-61663 80.585 -68.738 112.331 -0.46 0.266 -0.847 0.0
-61664 80.585 -68.844 112.331 -0.46 -0.266 -0.847 0.0
-61665 80.677 -68.897 112.331 0.0 -0.531 -0.847 0.0
-61666 80.769 -68.844 112.331 0.46 -0.266 -0.847 0.0
-61667 80.843 -68.791 112.389 0.831 0.0 -0.556 0.0
-61668 80.76 -68.647 112.389 0.415 0.72 -0.556 0.0
-61669 80.594 -68.647 112.389 -0.415 0.72 -0.556 0.0
-61670 80.511 -68.791 112.389 -0.831 -0.0 -0.556 0.0
-61671 80.594 -68.935 112.389 -0.415 -0.72 -0.556 0.0
-61672 80.76 -68.935 112.389 0.415 -0.72 -0.556 0.0
-61673 80.842 -68.695 112.441 0.827 0.478 -0.296 0.0
-61674 80.677 -68.6 112.441 0.0 0.955 -0.296 0.0
-61675 80.512 -68.695 112.441 -0.827 0.478 -0.296 0.0
-61676 80.512 -68.887 112.441 -0.827 -0.478 -0.296 0.0
-61677 80.677 -68.982 112.441 0.0 -0.955 -0.296 0.0
-61678 80.842 -68.887 112.441 0.827 -0.478 -0.296 0.0
-61679 80.868 -68.791 112.559 0.955 0.0 0.296 0.0
-61680 80.773 -68.626 112.559 0.478 0.827 0.296 0.0
-61681 80.581 -68.626 112.559 -0.478 0.827 0.296 0.0
-61682 80.486 -68.791 112.559 -0.955 -0.0 0.296 0.0
-61683 80.581 -68.956 112.559 -0.478 -0.827 0.296 0.0
-61684 80.773 -68.956 112.559 0.478 -0.827 0.296 0.0
-61685 80.821 -68.708 112.611 0.719 0.415 0.558 0.0
-61686 80.677 -68.625 112.611 0.0 0.83 0.558 0.0
-61687 80.533 -68.708 112.611 -0.719 0.415 0.558 0.0
-61688 80.533 -68.874 112.611 -0.719 -0.415 0.558 0.0
-61689 80.677 -68.957 112.611 0.0 -0.83 0.558 0.0
-61690 80.821 -68.874 112.611 0.719 -0.415 0.558 0.0
-1991 111 8 31
-61691 80.677 -68.791 103.6 -0.0 -0.0 -1.0 0.0
-61692 80.769 -68.738 103.631 0.46 0.266 -0.847 0.0
-61693 80.677 -68.685 103.631 0.0 0.531 -0.847 0.0
-61694 80.585 -68.738 103.631 -0.46 0.266 -0.847 0.0
-61695 80.585 -68.844 103.631 -0.46 -0.266 -0.847 0.0
-61696 80.677 -68.897 103.631 0.0 -0.531 -0.847 0.0
-61697 80.769 -68.844 103.631 0.46 -0.266 -0.847 0.0
-61698 80.843 -68.791 103.689 0.831 0.0 -0.556 0.0
-61699 80.76 -68.647 103.689 0.415 0.72 -0.556 0.0
-61700 80.594 -68.647 103.689 -0.415 0.72 -0.556 0.0
-61701 80.511 -68.791 103.689 -0.831 -0.0 -0.556 0.0
-61702 80.594 -68.935 103.689 -0.415 -0.72 -0.556 0.0
-61703 80.76 -68.935 103.689 0.415 -0.72 -0.556 0.0
-61704 80.842 -68.695 103.741 0.827 0.478 -0.296 0.0
-61705 80.677 -68.6 103.741 0.0 0.955 -0.296 0.0
-61706 80.512 -68.695 103.741 -0.827 0.478 -0.296 0.0
-61707 80.512 -68.887 103.741 -0.827 -0.478 -0.296 0.0
-61708 80.677 -68.982 103.741 0.0 -0.955 -0.296 0.0
-61709 80.842 -68.887 103.741 0.827 -0.478 -0.296 0.0
-61710 80.868 -68.791 103.859 0.955 0.0 0.296 0.0
-61711 80.773 -68.626 103.859 0.478 0.827 0.296 0.0
-61712 80.581 -68.626 103.859 -0.478 0.827 0.296 0.0
-61713 80.486 -68.791 103.859 -0.955 -0.0 0.296 0.0
-61714 80.581 -68.956 103.859 -0.478 -0.827 0.296 0.0
-61715 80.773 -68.956 103.859 0.478 -0.827 0.296 0.0
-61716 80.821 -68.708 103.911 0.719 0.415 0.558 0.0
-61717 80.677 -68.625 103.911 0.0 0.83 0.558 0.0
-61718 80.533 -68.708 103.911 -0.719 0.415 0.558 0.0
-61719 80.533 -68.874 103.911 -0.719 -0.415 0.558 0.0
-61720 80.677 -68.957 103.911 0.0 -0.83 0.558 0.0
-61721 80.821 -68.874 103.911 0.719 -0.415 0.558 0.0
-1992 111 7 31
-61722 80.677 -68.791 94.2 -0.0 -0.0 -1.0 0.0
-61723 80.769 -68.738 94.231 0.46 0.266 -0.847 0.0
-61724 80.677 -68.685 94.231 0.0 0.531 -0.847 0.0
-61725 80.585 -68.738 94.231 -0.46 0.266 -0.847 0.0
-61726 80.585 -68.844 94.231 -0.46 -0.266 -0.847 0.0
-61727 80.677 -68.897 94.231 0.0 -0.531 -0.847 0.0
-61728 80.769 -68.844 94.231 0.46 -0.266 -0.847 0.0
-61729 80.843 -68.791 94.289 0.831 0.0 -0.556 0.0
-61730 80.76 -68.647 94.289 0.415 0.72 -0.556 0.0
-61731 80.594 -68.647 94.289 -0.415 0.72 -0.556 0.0
-61732 80.511 -68.791 94.289 -0.831 -0.0 -0.556 0.0
-61733 80.594 -68.935 94.289 -0.415 -0.72 -0.556 0.0
-61734 80.76 -68.935 94.289 0.415 -0.72 -0.556 0.0
-61735 80.842 -68.695 94.341 0.827 0.478 -0.296 0.0
-61736 80.677 -68.6 94.341 0.0 0.955 -0.296 0.0
-61737 80.512 -68.695 94.341 -0.827 0.478 -0.296 0.0
-61738 80.512 -68.887 94.341 -0.827 -0.478 -0.296 0.0
-61739 80.677 -68.982 94.341 0.0 -0.955 -0.296 0.0
-61740 80.842 -68.887 94.341 0.827 -0.478 -0.296 0.0
-61741 80.868 -68.791 94.459 0.955 0.0 0.296 0.0
-61742 80.773 -68.626 94.459 0.478 0.827 0.296 0.0
-61743 80.581 -68.626 94.459 -0.478 0.827 0.296 0.0
-61744 80.486 -68.791 94.459 -0.955 -0.0 0.296 0.0
-61745 80.581 -68.956 94.459 -0.478 -0.827 0.296 0.0
-61746 80.773 -68.956 94.459 0.478 -0.827 0.296 0.0
-61747 80.821 -68.708 94.511 0.719 0.415 0.558 0.0
-61748 80.677 -68.625 94.511 0.0 0.83 0.558 0.0
-61749 80.533 -68.708 94.511 -0.719 0.415 0.558 0.0
-61750 80.533 -68.874 94.511 -0.719 -0.415 0.558 0.0
-61751 80.677 -68.957 94.511 0.0 -0.83 0.558 0.0
-61752 80.821 -68.874 94.511 0.719 -0.415 0.558 0.0
-1993 111 6 31
-61753 80.677 -68.791 83.3 -0.0 -0.0 -1.0 0.0
-61754 80.769 -68.738 83.331 0.46 0.266 -0.847 0.0
-61755 80.677 -68.685 83.331 0.0 0.531 -0.847 0.0
-61756 80.585 -68.738 83.331 -0.46 0.266 -0.847 0.0
-61757 80.585 -68.844 83.331 -0.46 -0.266 -0.847 0.0
-61758 80.677 -68.897 83.331 0.0 -0.531 -0.847 0.0
-61759 80.769 -68.844 83.331 0.46 -0.266 -0.847 0.0
-61760 80.843 -68.791 83.389 0.831 0.0 -0.556 0.0
-61761 80.76 -68.647 83.389 0.415 0.72 -0.556 0.0
-61762 80.594 -68.647 83.389 -0.415 0.72 -0.556 0.0
-61763 80.511 -68.791 83.389 -0.831 -0.0 -0.556 0.0
-61764 80.594 -68.935 83.389 -0.415 -0.72 -0.556 0.0
-61765 80.76 -68.935 83.389 0.415 -0.72 -0.556 0.0
-61766 80.842 -68.695 83.441 0.827 0.478 -0.296 0.0
-61767 80.677 -68.6 83.441 0.0 0.955 -0.296 0.0
-61768 80.512 -68.695 83.441 -0.827 0.478 -0.296 0.0
-61769 80.512 -68.887 83.441 -0.827 -0.478 -0.296 0.0
-61770 80.677 -68.982 83.441 0.0 -0.955 -0.296 0.0
-61771 80.842 -68.887 83.441 0.827 -0.478 -0.296 0.0
-61772 80.868 -68.791 83.559 0.955 0.0 0.296 0.0
-61773 80.773 -68.626 83.559 0.478 0.827 0.296 0.0
-61774 80.581 -68.626 83.559 -0.478 0.827 0.296 0.0
-61775 80.486 -68.791 83.559 -0.955 -0.0 0.296 0.0
-61776 80.581 -68.956 83.559 -0.478 -0.827 0.296 0.0
-61777 80.773 -68.956 83.559 0.478 -0.827 0.296 0.0
-61778 80.821 -68.708 83.611 0.719 0.415 0.558 0.0
-61779 80.677 -68.625 83.611 0.0 0.83 0.558 0.0
-61780 80.533 -68.708 83.611 -0.719 0.415 0.558 0.0
-61781 80.533 -68.874 83.611 -0.719 -0.415 0.558 0.0
-61782 80.677 -68.957 83.611 0.0 -0.83 0.558 0.0
-61783 80.821 -68.874 83.611 0.719 -0.415 0.558 0.0
-1994 111 5 31
-61784 80.677 -68.791 73.9 -0.0 -0.0 -1.0 0.0
-61785 80.769 -68.738 73.931 0.46 0.266 -0.847 0.0
-61786 80.677 -68.685 73.931 0.0 0.531 -0.847 0.0
-61787 80.585 -68.738 73.931 -0.46 0.266 -0.847 0.0
-61788 80.585 -68.844 73.931 -0.46 -0.266 -0.847 0.0
-61789 80.677 -68.897 73.931 0.0 -0.531 -0.847 0.0
-61790 80.769 -68.844 73.931 0.46 -0.266 -0.847 0.0
-61791 80.843 -68.791 73.989 0.831 0.0 -0.556 0.0
-61792 80.76 -68.647 73.989 0.415 0.72 -0.556 0.0
-61793 80.594 -68.647 73.989 -0.415 0.72 -0.556 0.0
-61794 80.511 -68.791 73.989 -0.831 -0.0 -0.556 0.0
-61795 80.594 -68.935 73.989 -0.415 -0.72 -0.556 0.0
-61796 80.76 -68.935 73.989 0.415 -0.72 -0.556 0.0
-61797 80.842 -68.695 74.041 0.827 0.478 -0.296 0.0
-61798 80.677 -68.6 74.041 0.0 0.955 -0.296 0.0
-61799 80.512 -68.695 74.041 -0.827 0.478 -0.296 0.0
-61800 80.512 -68.887 74.041 -0.827 -0.478 -0.296 0.0
-61801 80.677 -68.982 74.041 0.0 -0.955 -0.296 0.0
-61802 80.842 -68.887 74.041 0.827 -0.478 -0.296 0.0
-61803 80.868 -68.791 74.159 0.955 0.0 0.296 0.0
-61804 80.773 -68.626 74.159 0.478 0.827 0.296 0.0
-61805 80.581 -68.626 74.159 -0.478 0.827 0.296 0.0
-61806 80.486 -68.791 74.159 -0.955 -0.0 0.296 0.0
-61807 80.581 -68.956 74.159 -0.478 -0.827 0.296 0.0
-61808 80.773 -68.956 74.159 0.478 -0.827 0.296 0.0
-61809 80.821 -68.708 74.211 0.719 0.415 0.558 0.0
-61810 80.677 -68.625 74.211 0.0 0.83 0.558 0.0
-61811 80.533 -68.708 74.211 -0.719 0.415 0.558 0.0
-61812 80.533 -68.874 74.211 -0.719 -0.415 0.558 0.0
-61813 80.677 -68.957 74.211 0.0 -0.83 0.558 0.0
-61814 80.821 -68.874 74.211 0.719 -0.415 0.558 0.0
-1995 111 4 31
-61815 80.677 -68.791 65.2 -0.0 -0.0 -1.0 0.0
-61816 80.769 -68.738 65.231 0.46 0.266 -0.847 0.0
-61817 80.677 -68.685 65.231 0.0 0.531 -0.847 0.0
-61818 80.585 -68.738 65.231 -0.46 0.266 -0.847 0.0
-61819 80.585 -68.844 65.231 -0.46 -0.266 -0.847 0.0
-61820 80.677 -68.897 65.231 0.0 -0.531 -0.847 0.0
-61821 80.769 -68.844 65.231 0.46 -0.266 -0.847 0.0
-61822 80.843 -68.791 65.289 0.831 0.0 -0.556 0.0
-61823 80.76 -68.647 65.289 0.415 0.72 -0.556 0.0
-61824 80.594 -68.647 65.289 -0.415 0.72 -0.556 0.0
-61825 80.511 -68.791 65.289 -0.831 -0.0 -0.556 0.0
-61826 80.594 -68.935 65.289 -0.415 -0.72 -0.556 0.0
-61827 80.76 -68.935 65.289 0.415 -0.72 -0.556 0.0
-61828 80.842 -68.695 65.341 0.827 0.478 -0.296 0.0
-61829 80.677 -68.6 65.341 0.0 0.955 -0.296 0.0
-61830 80.512 -68.695 65.341 -0.827 0.478 -0.296 0.0
-61831 80.512 -68.887 65.341 -0.827 -0.478 -0.296 0.0
-61832 80.677 -68.982 65.341 0.0 -0.955 -0.296 0.0
-61833 80.842 -68.887 65.341 0.827 -0.478 -0.296 0.0
-61834 80.868 -68.791 65.459 0.955 0.0 0.296 0.0
-61835 80.773 -68.626 65.459 0.478 0.827 0.296 0.0
-61836 80.581 -68.626 65.459 -0.478 0.827 0.296 0.0
-61837 80.486 -68.791 65.459 -0.955 -0.0 0.296 0.0
-61838 80.581 -68.956 65.459 -0.478 -0.827 0.296 0.0
-61839 80.773 -68.956 65.459 0.478 -0.827 0.296 0.0
-61840 80.821 -68.708 65.511 0.719 0.415 0.558 0.0
-61841 80.677 -68.625 65.511 0.0 0.83 0.558 0.0
-61842 80.533 -68.708 65.511 -0.719 0.415 0.558 0.0
-61843 80.533 -68.874 65.511 -0.719 -0.415 0.558 0.0
-61844 80.677 -68.957 65.511 0.0 -0.83 0.558 0.0
-61845 80.821 -68.874 65.511 0.719 -0.415 0.558 0.0
-1996 111 3 31
-61846 80.677 -68.791 55.8 -0.0 -0.0 -1.0 0.0
-61847 80.769 -68.738 55.831 0.46 0.266 -0.847 0.0
-61848 80.677 -68.685 55.831 0.0 0.531 -0.847 0.0
-61849 80.585 -68.738 55.831 -0.46 0.266 -0.847 0.0
-61850 80.585 -68.844 55.831 -0.46 -0.266 -0.847 0.0
-61851 80.677 -68.897 55.831 0.0 -0.531 -0.847 0.0
-61852 80.769 -68.844 55.831 0.46 -0.266 -0.847 0.0
-61853 80.843 -68.791 55.889 0.831 0.0 -0.556 0.0
-61854 80.76 -68.647 55.889 0.415 0.72 -0.556 0.0
-61855 80.594 -68.647 55.889 -0.415 0.72 -0.556 0.0
-61856 80.511 -68.791 55.889 -0.831 -0.0 -0.556 0.0
-61857 80.594 -68.935 55.889 -0.415 -0.72 -0.556 0.0
-61858 80.76 -68.935 55.889 0.415 -0.72 -0.556 0.0
-61859 80.842 -68.695 55.941 0.827 0.478 -0.296 0.0
-61860 80.677 -68.6 55.941 0.0 0.955 -0.296 0.0
-61861 80.512 -68.695 55.941 -0.827 0.478 -0.296 0.0
-61862 80.512 -68.887 55.941 -0.827 -0.478 -0.296 0.0
-61863 80.677 -68.982 55.941 0.0 -0.955 -0.296 0.0
-61864 80.842 -68.887 55.941 0.827 -0.478 -0.296 0.0
-61865 80.868 -68.791 56.059 0.955 0.0 0.296 0.0
-61866 80.773 -68.626 56.059 0.478 0.827 0.296 0.0
-61867 80.581 -68.626 56.059 -0.478 0.827 0.296 0.0
-61868 80.486 -68.791 56.059 -0.955 -0.0 0.296 0.0
-61869 80.581 -68.956 56.059 -0.478 -0.827 0.296 0.0
-61870 80.773 -68.956 56.059 0.478 -0.827 0.296 0.0
-61871 80.821 -68.708 56.111 0.719 0.415 0.558 0.0
-61872 80.677 -68.625 56.111 0.0 0.83 0.558 0.0
-61873 80.533 -68.708 56.111 -0.719 0.415 0.558 0.0
-61874 80.533 -68.874 56.111 -0.719 -0.415 0.558 0.0
-61875 80.677 -68.957 56.111 0.0 -0.83 0.558 0.0
-61876 80.821 -68.874 56.111 0.719 -0.415 0.558 0.0
-1997 111 2 31
-61877 80.677 -68.791 47.1 -0.0 -0.0 -1.0 0.0
-61878 80.769 -68.738 47.131 0.46 0.266 -0.847 0.0
-61879 80.677 -68.685 47.131 0.0 0.531 -0.847 0.0
-61880 80.585 -68.738 47.131 -0.46 0.266 -0.847 0.0
-61881 80.585 -68.844 47.131 -0.46 -0.266 -0.847 0.0
-61882 80.677 -68.897 47.131 0.0 -0.531 -0.847 0.0
-61883 80.769 -68.844 47.131 0.46 -0.266 -0.847 0.0
-61884 80.843 -68.791 47.189 0.831 0.0 -0.556 0.0
-61885 80.76 -68.647 47.189 0.415 0.72 -0.556 0.0
-61886 80.594 -68.647 47.189 -0.415 0.72 -0.556 0.0
-61887 80.511 -68.791 47.189 -0.831 -0.0 -0.556 0.0
-61888 80.594 -68.935 47.189 -0.415 -0.72 -0.556 0.0
-61889 80.76 -68.935 47.189 0.415 -0.72 -0.556 0.0
-61890 80.842 -68.695 47.241 0.827 0.478 -0.296 0.0
-61891 80.677 -68.6 47.241 0.0 0.955 -0.296 0.0
-61892 80.512 -68.695 47.241 -0.827 0.478 -0.296 0.0
-61893 80.512 -68.887 47.241 -0.827 -0.478 -0.296 0.0
-61894 80.677 -68.982 47.241 0.0 -0.955 -0.296 0.0
-61895 80.842 -68.887 47.241 0.827 -0.478 -0.296 0.0
-61896 80.868 -68.791 47.359 0.955 0.0 0.296 0.0
-61897 80.773 -68.626 47.359 0.478 0.827 0.296 0.0
-61898 80.581 -68.626 47.359 -0.478 0.827 0.296 0.0
-61899 80.486 -68.791 47.359 -0.955 -0.0 0.296 0.0
-61900 80.581 -68.956 47.359 -0.478 -0.827 0.296 0.0
-61901 80.773 -68.956 47.359 0.478 -0.827 0.296 0.0
-61902 80.821 -68.708 47.411 0.719 0.415 0.558 0.0
-61903 80.677 -68.625 47.411 0.0 0.83 0.558 0.0
-61904 80.533 -68.708 47.411 -0.719 0.415 0.558 0.0
-61905 80.533 -68.874 47.411 -0.719 -0.415 0.558 0.0
-61906 80.677 -68.957 47.411 0.0 -0.83 0.558 0.0
-61907 80.821 -68.874 47.411 0.719 -0.415 0.558 0.0
-1998 111 1 31
-61908 80.677 -68.791 37.7 -0.0 -0.0 -1.0 0.0
-61909 80.769 -68.738 37.731 0.46 0.266 -0.847 0.0
-61910 80.677 -68.685 37.731 0.0 0.531 -0.847 0.0
-61911 80.585 -68.738 37.731 -0.46 0.266 -0.847 0.0
-61912 80.585 -68.844 37.731 -0.46 -0.266 -0.847 0.0
-61913 80.677 -68.897 37.731 0.0 -0.531 -0.847 0.0
-61914 80.769 -68.844 37.731 0.46 -0.266 -0.847 0.0
-61915 80.843 -68.791 37.789 0.831 0.0 -0.556 0.0
-61916 80.76 -68.647 37.789 0.415 0.72 -0.556 0.0
-61917 80.594 -68.647 37.789 -0.415 0.72 -0.556 0.0
-61918 80.511 -68.791 37.789 -0.831 -0.0 -0.556 0.0
-61919 80.594 -68.935 37.789 -0.415 -0.72 -0.556 0.0
-61920 80.76 -68.935 37.789 0.415 -0.72 -0.556 0.0
-61921 80.842 -68.695 37.841 0.827 0.478 -0.296 0.0
-61922 80.677 -68.6 37.841 0.0 0.955 -0.296 0.0
-61923 80.512 -68.695 37.841 -0.827 0.478 -0.296 0.0
-61924 80.512 -68.887 37.841 -0.827 -0.478 -0.296 0.0
-61925 80.677 -68.982 37.841 0.0 -0.955 -0.296 0.0
-61926 80.842 -68.887 37.841 0.827 -0.478 -0.296 0.0
-61927 80.868 -68.791 37.959 0.955 0.0 0.296 0.0
-61928 80.773 -68.626 37.959 0.478 0.827 0.296 0.0
-61929 80.581 -68.626 37.959 -0.478 0.827 0.296 0.0
-61930 80.486 -68.791 37.959 -0.955 -0.0 0.296 0.0
-61931 80.581 -68.956 37.959 -0.478 -0.827 0.296 0.0
-61932 80.773 -68.956 37.959 0.478 -0.827 0.296 0.0
-61933 80.821 -68.708 38.011 0.719 0.415 0.558 0.0
-61934 80.677 -68.625 38.011 0.0 0.83 0.558 0.0
-61935 80.533 -68.708 38.011 -0.719 0.415 0.558 0.0
-61936 80.533 -68.874 38.011 -0.719 -0.415 0.558 0.0
-61937 80.677 -68.957 38.011 0.0 -0.83 0.558 0.0
-61938 80.821 -68.874 38.011 0.719 -0.415 0.558 0.0
-1999 112 18 31
-61939 101.532 -69.112 196.3 -0.0 -0.0 -1.0 0.0
-61940 101.624 -69.059 196.331 0.46 0.266 -0.847 0.0
-61941 101.532 -69.006 196.331 0.0 0.531 -0.847 0.0
-61942 101.44 -69.059 196.331 -0.46 0.266 -0.847 0.0
-61943 101.44 -69.165 196.331 -0.46 -0.266 -0.847 0.0
-61944 101.532 -69.218 196.331 0.0 -0.531 -0.847 0.0
-61945 101.624 -69.165 196.331 0.46 -0.266 -0.847 0.0
-61946 101.698 -69.112 196.389 0.831 0.0 -0.556 0.0
-61947 101.615 -68.968 196.389 0.415 0.72 -0.556 0.0
-61948 101.449 -68.968 196.389 -0.415 0.72 -0.556 0.0
-61949 101.366 -69.112 196.389 -0.831 -0.0 -0.556 0.0
-61950 101.449 -69.256 196.389 -0.415 -0.72 -0.556 0.0
-61951 101.615 -69.256 196.389 0.415 -0.72 -0.556 0.0
-61952 101.697 -69.016 196.441 0.827 0.478 -0.296 0.0
-61953 101.532 -68.921 196.441 0.0 0.955 -0.296 0.0
-61954 101.367 -69.016 196.441 -0.827 0.478 -0.296 0.0
-61955 101.367 -69.208 196.441 -0.827 -0.478 -0.296 0.0
-61956 101.532 -69.303 196.441 0.0 -0.955 -0.296 0.0
-61957 101.697 -69.208 196.441 0.827 -0.478 -0.296 0.0
-61958 101.723 -69.112 196.559 0.955 0.0 0.296 0.0
-61959 101.628 -68.947 196.559 0.478 0.827 0.296 0.0
-61960 101.436 -68.947 196.559 -0.478 0.827 0.296 0.0
-61961 101.341 -69.112 196.559 -0.955 -0.0 0.296 0.0
-61962 101.436 -69.277 196.559 -0.478 -0.827 0.296 0.0
-61963 101.628 -69.277 196.559 0.478 -0.827 0.296 0.0
-61964 101.676 -69.029 196.611 0.719 0.415 0.558 0.0
-61965 101.532 -68.946 196.611 0.0 0.83 0.558 0.0
-61966 101.388 -69.029 196.611 -0.719 0.415 0.558 0.0
-61967 101.388 -69.195 196.611 -0.719 -0.415 0.558 0.0
-61968 101.532 -69.278 196.611 0.0 -0.83 0.558 0.0
-61969 101.676 -69.195 196.611 0.719 -0.415 0.558 0.0
-2000 112 17 31
-61970 101.532 -69.112 186.9 -0.0 -0.0 -1.0 0.0
-61971 101.624 -69.059 186.931 0.46 0.266 -0.847 0.0
-61972 101.532 -69.006 186.931 0.0 0.531 -0.847 0.0
-61973 101.44 -69.059 186.931 -0.46 0.266 -0.847 0.0
-61974 101.44 -69.165 186.931 -0.46 -0.266 -0.847 0.0
-61975 101.532 -69.218 186.931 0.0 -0.531 -0.847 0.0
-61976 101.624 -69.165 186.931 0.46 -0.266 -0.847 0.0
-61977 101.698 -69.112 186.989 0.831 0.0 -0.556 0.0
-61978 101.615 -68.968 186.989 0.415 0.72 -0.556 0.0
-61979 101.449 -68.968 186.989 -0.415 0.72 -0.556 0.0
-61980 101.366 -69.112 186.989 -0.831 -0.0 -0.556 0.0
-61981 101.449 -69.256 186.989 -0.415 -0.72 -0.556 0.0
-61982 101.615 -69.256 186.989 0.415 -0.72 -0.556 0.0
-61983 101.697 -69.016 187.041 0.827 0.478 -0.296 0.0
-61984 101.532 -68.921 187.041 0.0 0.955 -0.296 0.0
-61985 101.367 -69.016 187.041 -0.827 0.478 -0.296 0.0
-61986 101.367 -69.208 187.041 -0.827 -0.478 -0.296 0.0
-61987 101.532 -69.303 187.041 0.0 -0.955 -0.296 0.0
-61988 101.697 -69.208 187.041 0.827 -0.478 -0.296 0.0
-61989 101.723 -69.112 187.159 0.955 0.0 0.296 0.0
-61990 101.628 -68.947 187.159 0.478 0.827 0.296 0.0
-61991 101.436 -68.947 187.159 -0.478 0.827 0.296 0.0
-61992 101.341 -69.112 187.159 -0.955 -0.0 0.296 0.0
-61993 101.436 -69.277 187.159 -0.478 -0.827 0.296 0.0
-61994 101.628 -69.277 187.159 0.478 -0.827 0.296 0.0
-61995 101.676 -69.029 187.211 0.719 0.415 0.558 0.0
-61996 101.532 -68.946 187.211 0.0 0.83 0.558 0.0
-61997 101.388 -69.029 187.211 -0.719 0.415 0.558 0.0
-61998 101.388 -69.195 187.211 -0.719 -0.415 0.558 0.0
-61999 101.532 -69.278 187.211 0.0 -0.83 0.558 0.0
-62000 101.676 -69.195 187.211 0.719 -0.415 0.558 0.0
-2001 112 16 31
-62001 101.532 -69.112 178.2 -0.0 -0.0 -1.0 0.0
-62002 101.624 -69.059 178.231 0.46 0.266 -0.847 0.0
-62003 101.532 -69.006 178.231 0.0 0.531 -0.847 0.0
-62004 101.44 -69.059 178.231 -0.46 0.266 -0.847 0.0
-62005 101.44 -69.165 178.231 -0.46 -0.266 -0.847 0.0
-62006 101.532 -69.218 178.231 0.0 -0.531 -0.847 0.0
-62007 101.624 -69.165 178.231 0.46 -0.266 -0.847 0.0
-62008 101.698 -69.112 178.289 0.831 0.0 -0.556 0.0
-62009 101.615 -68.968 178.289 0.415 0.72 -0.556 0.0
-62010 101.449 -68.968 178.289 -0.415 0.72 -0.556 0.0
-62011 101.366 -69.112 178.289 -0.831 -0.0 -0.556 0.0
-62012 101.449 -69.256 178.289 -0.415 -0.72 -0.556 0.0
-62013 101.615 -69.256 178.289 0.415 -0.72 -0.556 0.0
-62014 101.697 -69.016 178.341 0.827 0.478 -0.296 0.0
-62015 101.532 -68.921 178.341 0.0 0.955 -0.296 0.0
-62016 101.367 -69.016 178.341 -0.827 0.478 -0.296 0.0
-62017 101.367 -69.208 178.341 -0.827 -0.478 -0.296 0.0
-62018 101.532 -69.303 178.341 0.0 -0.955 -0.296 0.0
-62019 101.697 -69.208 178.341 0.827 -0.478 -0.296 0.0
-62020 101.723 -69.112 178.459 0.955 0.0 0.296 0.0
-62021 101.628 -68.947 178.459 0.478 0.827 0.296 0.0
-62022 101.436 -68.947 178.459 -0.478 0.827 0.296 0.0
-62023 101.341 -69.112 178.459 -0.955 -0.0 0.296 0.0
-62024 101.436 -69.277 178.459 -0.478 -0.827 0.296 0.0
-62025 101.628 -69.277 178.459 0.478 -0.827 0.296 0.0
-62026 101.676 -69.029 178.511 0.719 0.415 0.558 0.0
-62027 101.532 -68.946 178.511 0.0 0.83 0.558 0.0
-62028 101.388 -69.029 178.511 -0.719 0.415 0.558 0.0
-62029 101.388 -69.195 178.511 -0.719 -0.415 0.558 0.0
-62030 101.532 -69.278 178.511 0.0 -0.83 0.558 0.0
-62031 101.676 -69.195 178.511 0.719 -0.415 0.558 0.0
-2002 112 15 31
-62032 101.532 -69.112 168.8 -0.0 -0.0 -1.0 0.0
-62033 101.624 -69.059 168.831 0.46 0.266 -0.847 0.0
-62034 101.532 -69.006 168.831 0.0 0.531 -0.847 0.0
-62035 101.44 -69.059 168.831 -0.46 0.266 -0.847 0.0
-62036 101.44 -69.165 168.831 -0.46 -0.266 -0.847 0.0
-62037 101.532 -69.218 168.831 0.0 -0.531 -0.847 0.0
-62038 101.624 -69.165 168.831 0.46 -0.266 -0.847 0.0
-62039 101.698 -69.112 168.889 0.831 0.0 -0.556 0.0
-62040 101.615 -68.968 168.889 0.415 0.72 -0.556 0.0
-62041 101.449 -68.968 168.889 -0.415 0.72 -0.556 0.0
-62042 101.366 -69.112 168.889 -0.831 -0.0 -0.556 0.0
-62043 101.449 -69.256 168.889 -0.415 -0.72 -0.556 0.0
-62044 101.615 -69.256 168.889 0.415 -0.72 -0.556 0.0
-62045 101.697 -69.016 168.941 0.827 0.478 -0.296 0.0
-62046 101.532 -68.921 168.941 0.0 0.955 -0.296 0.0
-62047 101.367 -69.016 168.941 -0.827 0.478 -0.296 0.0
-62048 101.367 -69.208 168.941 -0.827 -0.478 -0.296 0.0
-62049 101.532 -69.303 168.941 0.0 -0.955 -0.296 0.0
-62050 101.697 -69.208 168.941 0.827 -0.478 -0.296 0.0
-62051 101.723 -69.112 169.059 0.955 0.0 0.296 0.0
-62052 101.628 -68.947 169.059 0.478 0.827 0.296 0.0
-62053 101.436 -68.947 169.059 -0.478 0.827 0.296 0.0
-62054 101.341 -69.112 169.059 -0.955 -0.0 0.296 0.0
-62055 101.436 -69.277 169.059 -0.478 -0.827 0.296 0.0
-62056 101.628 -69.277 169.059 0.478 -0.827 0.296 0.0
-62057 101.676 -69.029 169.111 0.719 0.415 0.558 0.0
-62058 101.532 -68.946 169.111 0.0 0.83 0.558 0.0
-62059 101.388 -69.029 169.111 -0.719 0.415 0.558 0.0
-62060 101.388 -69.195 169.111 -0.719 -0.415 0.558 0.0
-62061 101.532 -69.278 169.111 0.0 -0.83 0.558 0.0
-62062 101.676 -69.195 169.111 0.719 -0.415 0.558 0.0
-2003 112 14 31
-62063 101.532 -69.112 160.1 -0.0 -0.0 -1.0 0.0
-62064 101.624 -69.059 160.131 0.46 0.266 -0.847 0.0
-62065 101.532 -69.006 160.131 0.0 0.531 -0.847 0.0
-62066 101.44 -69.059 160.131 -0.46 0.266 -0.847 0.0
-62067 101.44 -69.165 160.131 -0.46 -0.266 -0.847 0.0
-62068 101.532 -69.218 160.131 0.0 -0.531 -0.847 0.0
-62069 101.624 -69.165 160.131 0.46 -0.266 -0.847 0.0
-62070 101.698 -69.112 160.189 0.831 0.0 -0.556 0.0
-62071 101.615 -68.968 160.189 0.415 0.72 -0.556 0.0
-62072 101.449 -68.968 160.189 -0.415 0.72 -0.556 0.0
-62073 101.366 -69.112 160.189 -0.831 -0.0 -0.556 0.0
-62074 101.449 -69.256 160.189 -0.415 -0.72 -0.556 0.0
-62075 101.615 -69.256 160.189 0.415 -0.72 -0.556 0.0
-62076 101.697 -69.016 160.241 0.827 0.478 -0.296 0.0
-62077 101.532 -68.921 160.241 0.0 0.955 -0.296 0.0
-62078 101.367 -69.016 160.241 -0.827 0.478 -0.296 0.0
-62079 101.367 -69.208 160.241 -0.827 -0.478 -0.296 0.0
-62080 101.532 -69.303 160.241 0.0 -0.955 -0.296 0.0
-62081 101.697 -69.208 160.241 0.827 -0.478 -0.296 0.0
-62082 101.723 -69.112 160.359 0.955 0.0 0.296 0.0
-62083 101.628 -68.947 160.359 0.478 0.827 0.296 0.0
-62084 101.436 -68.947 160.359 -0.478 0.827 0.296 0.0
-62085 101.341 -69.112 160.359 -0.955 -0.0 0.296 0.0
-62086 101.436 -69.277 160.359 -0.478 -0.827 0.296 0.0
-62087 101.628 -69.277 160.359 0.478 -0.827 0.296 0.0
-62088 101.676 -69.029 160.411 0.719 0.415 0.558 0.0
-62089 101.532 -68.946 160.411 0.0 0.83 0.558 0.0
-62090 101.388 -69.029 160.411 -0.719 0.415 0.558 0.0
-62091 101.388 -69.195 160.411 -0.719 -0.415 0.558 0.0
-62092 101.532 -69.278 160.411 0.0 -0.83 0.558 0.0
-62093 101.676 -69.195 160.411 0.719 -0.415 0.558 0.0
-2004 112 13 31
-62094 101.532 -69.112 150.7 -0.0 -0.0 -1.0 0.0
-62095 101.624 -69.059 150.731 0.46 0.266 -0.847 0.0
-62096 101.532 -69.006 150.731 0.0 0.531 -0.847 0.0
-62097 101.44 -69.059 150.731 -0.46 0.266 -0.847 0.0
-62098 101.44 -69.165 150.731 -0.46 -0.266 -0.847 0.0
-62099 101.532 -69.218 150.731 0.0 -0.531 -0.847 0.0
-62100 101.624 -69.165 150.731 0.46 -0.266 -0.847 0.0
-62101 101.698 -69.112 150.789 0.831 0.0 -0.556 0.0
-62102 101.615 -68.968 150.789 0.415 0.72 -0.556 0.0
-62103 101.449 -68.968 150.789 -0.415 0.72 -0.556 0.0
-62104 101.366 -69.112 150.789 -0.831 -0.0 -0.556 0.0
-62105 101.449 -69.256 150.789 -0.415 -0.72 -0.556 0.0
-62106 101.615 -69.256 150.789 0.415 -0.72 -0.556 0.0
-62107 101.697 -69.016 150.841 0.827 0.478 -0.296 0.0
-62108 101.532 -68.921 150.841 0.0 0.955 -0.296 0.0
-62109 101.367 -69.016 150.841 -0.827 0.478 -0.296 0.0
-62110 101.367 -69.208 150.841 -0.827 -0.478 -0.296 0.0
-62111 101.532 -69.303 150.841 0.0 -0.955 -0.296 0.0
-62112 101.697 -69.208 150.841 0.827 -0.478 -0.296 0.0
-62113 101.723 -69.112 150.959 0.955 0.0 0.296 0.0
-62114 101.628 -68.947 150.959 0.478 0.827 0.296 0.0
-62115 101.436 -68.947 150.959 -0.478 0.827 0.296 0.0
-62116 101.341 -69.112 150.959 -0.955 -0.0 0.296 0.0
-62117 101.436 -69.277 150.959 -0.478 -0.827 0.296 0.0
-62118 101.628 -69.277 150.959 0.478 -0.827 0.296 0.0
-62119 101.676 -69.029 151.011 0.719 0.415 0.558 0.0
-62120 101.532 -68.946 151.011 0.0 0.83 0.558 0.0
-62121 101.388 -69.029 151.011 -0.719 0.415 0.558 0.0
-62122 101.388 -69.195 151.011 -0.719 -0.415 0.558 0.0
-62123 101.532 -69.278 151.011 0.0 -0.83 0.558 0.0
-62124 101.676 -69.195 151.011 0.719 -0.415 0.558 0.0
-2005 112 12 31
-62125 101.532 -69.112 139.8 -0.0 -0.0 -1.0 0.0
-62126 101.624 -69.059 139.831 0.46 0.266 -0.847 0.0
-62127 101.532 -69.006 139.831 0.0 0.531 -0.847 0.0
-62128 101.44 -69.059 139.831 -0.46 0.266 -0.847 0.0
-62129 101.44 -69.165 139.831 -0.46 -0.266 -0.847 0.0
-62130 101.532 -69.218 139.831 0.0 -0.531 -0.847 0.0
-62131 101.624 -69.165 139.831 0.46 -0.266 -0.847 0.0
-62132 101.698 -69.112 139.889 0.831 0.0 -0.556 0.0
-62133 101.615 -68.968 139.889 0.415 0.72 -0.556 0.0
-62134 101.449 -68.968 139.889 -0.415 0.72 -0.556 0.0
-62135 101.366 -69.112 139.889 -0.831 -0.0 -0.556 0.0
-62136 101.449 -69.256 139.889 -0.415 -0.72 -0.556 0.0
-62137 101.615 -69.256 139.889 0.415 -0.72 -0.556 0.0
-62138 101.697 -69.016 139.941 0.827 0.478 -0.296 0.0
-62139 101.532 -68.921 139.941 0.0 0.955 -0.296 0.0
-62140 101.367 -69.016 139.941 -0.827 0.478 -0.296 0.0
-62141 101.367 -69.208 139.941 -0.827 -0.478 -0.296 0.0
-62142 101.532 -69.303 139.941 0.0 -0.955 -0.296 0.0
-62143 101.697 -69.208 139.941 0.827 -0.478 -0.296 0.0
-62144 101.723 -69.112 140.059 0.955 0.0 0.296 0.0
-62145 101.628 -68.947 140.059 0.478 0.827 0.296 0.0
-62146 101.436 -68.947 140.059 -0.478 0.827 0.296 0.0
-62147 101.341 -69.112 140.059 -0.955 -0.0 0.296 0.0
-62148 101.436 -69.277 140.059 -0.478 -0.827 0.296 0.0
-62149 101.628 -69.277 140.059 0.478 -0.827 0.296 0.0
-62150 101.676 -69.029 140.111 0.719 0.415 0.558 0.0
-62151 101.532 -68.946 140.111 0.0 0.83 0.558 0.0
-62152 101.388 -69.029 140.111 -0.719 0.415 0.558 0.0
-62153 101.388 -69.195 140.111 -0.719 -0.415 0.558 0.0
-62154 101.532 -69.278 140.111 0.0 -0.83 0.558 0.0
-62155 101.676 -69.195 140.111 0.719 -0.415 0.558 0.0
-2006 112 11 31
-62156 101.532 -69.112 130.4 -0.0 -0.0 -1.0 0.0
-62157 101.624 -69.059 130.431 0.46 0.266 -0.847 0.0
-62158 101.532 -69.006 130.431 0.0 0.531 -0.847 0.0
-62159 101.44 -69.059 130.431 -0.46 0.266 -0.847 0.0
-62160 101.44 -69.165 130.431 -0.46 -0.266 -0.847 0.0
-62161 101.532 -69.218 130.431 0.0 -0.531 -0.847 0.0
-62162 101.624 -69.165 130.431 0.46 -0.266 -0.847 0.0
-62163 101.698 -69.112 130.489 0.831 0.0 -0.556 0.0
-62164 101.615 -68.968 130.489 0.415 0.72 -0.556 0.0
-62165 101.449 -68.968 130.489 -0.415 0.72 -0.556 0.0
-62166 101.366 -69.112 130.489 -0.831 -0.0 -0.556 0.0
-62167 101.449 -69.256 130.489 -0.415 -0.72 -0.556 0.0
-62168 101.615 -69.256 130.489 0.415 -0.72 -0.556 0.0
-62169 101.697 -69.016 130.541 0.827 0.478 -0.296 0.0
-62170 101.532 -68.921 130.541 0.0 0.955 -0.296 0.0
-62171 101.367 -69.016 130.541 -0.827 0.478 -0.296 0.0
-62172 101.367 -69.208 130.541 -0.827 -0.478 -0.296 0.0
-62173 101.532 -69.303 130.541 0.0 -0.955 -0.296 0.0
-62174 101.697 -69.208 130.541 0.827 -0.478 -0.296 0.0
-62175 101.723 -69.112 130.659 0.955 0.0 0.296 0.0
-62176 101.628 -68.947 130.659 0.478 0.827 0.296 0.0
-62177 101.436 -68.947 130.659 -0.478 0.827 0.296 0.0
-62178 101.341 -69.112 130.659 -0.955 -0.0 0.296 0.0
-62179 101.436 -69.277 130.659 -0.478 -0.827 0.296 0.0
-62180 101.628 -69.277 130.659 0.478 -0.827 0.296 0.0
-62181 101.676 -69.029 130.711 0.719 0.415 0.558 0.0
-62182 101.532 -68.946 130.711 0.0 0.83 0.558 0.0
-62183 101.388 -69.029 130.711 -0.719 0.415 0.558 0.0
-62184 101.388 -69.195 130.711 -0.719 -0.415 0.558 0.0
-62185 101.532 -69.278 130.711 0.0 -0.83 0.558 0.0
-62186 101.676 -69.195 130.711 0.719 -0.415 0.558 0.0
-2007 112 10 31
-62187 101.532 -69.112 121.7 -0.0 -0.0 -1.0 0.0
-62188 101.624 -69.059 121.731 0.46 0.266 -0.847 0.0
-62189 101.532 -69.006 121.731 0.0 0.531 -0.847 0.0
-62190 101.44 -69.059 121.731 -0.46 0.266 -0.847 0.0
-62191 101.44 -69.165 121.731 -0.46 -0.266 -0.847 0.0
-62192 101.532 -69.218 121.731 0.0 -0.531 -0.847 0.0
-62193 101.624 -69.165 121.731 0.46 -0.266 -0.847 0.0
-62194 101.698 -69.112 121.789 0.831 0.0 -0.556 0.0
-62195 101.615 -68.968 121.789 0.415 0.72 -0.556 0.0
-62196 101.449 -68.968 121.789 -0.415 0.72 -0.556 0.0
-62197 101.366 -69.112 121.789 -0.831 -0.0 -0.556 0.0
-62198 101.449 -69.256 121.789 -0.415 -0.72 -0.556 0.0
-62199 101.615 -69.256 121.789 0.415 -0.72 -0.556 0.0
-62200 101.697 -69.016 121.841 0.827 0.478 -0.296 0.0
-62201 101.532 -68.921 121.841 0.0 0.955 -0.296 0.0
-62202 101.367 -69.016 121.841 -0.827 0.478 -0.296 0.0
-62203 101.367 -69.208 121.841 -0.827 -0.478 -0.296 0.0
-62204 101.532 -69.303 121.841 0.0 -0.955 -0.296 0.0
-62205 101.697 -69.208 121.841 0.827 -0.478 -0.296 0.0
-62206 101.723 -69.112 121.959 0.955 0.0 0.296 0.0
-62207 101.628 -68.947 121.959 0.478 0.827 0.296 0.0
-62208 101.436 -68.947 121.959 -0.478 0.827 0.296 0.0
-62209 101.341 -69.112 121.959 -0.955 -0.0 0.296 0.0
-62210 101.436 -69.277 121.959 -0.478 -0.827 0.296 0.0
-62211 101.628 -69.277 121.959 0.478 -0.827 0.296 0.0
-62212 101.676 -69.029 122.011 0.719 0.415 0.558 0.0
-62213 101.532 -68.946 122.011 0.0 0.83 0.558 0.0
-62214 101.388 -69.029 122.011 -0.719 0.415 0.558 0.0
-62215 101.388 -69.195 122.011 -0.719 -0.415 0.558 0.0
-62216 101.532 -69.278 122.011 0.0 -0.83 0.558 0.0
-62217 101.676 -69.195 122.011 0.719 -0.415 0.558 0.0
-2008 112 9 31
-62218 101.532 -69.112 112.3 -0.0 -0.0 -1.0 0.0
-62219 101.624 -69.059 112.331 0.46 0.266 -0.847 0.0
-62220 101.532 -69.006 112.331 0.0 0.531 -0.847 0.0
-62221 101.44 -69.059 112.331 -0.46 0.266 -0.847 0.0
-62222 101.44 -69.165 112.331 -0.46 -0.266 -0.847 0.0
-62223 101.532 -69.218 112.331 0.0 -0.531 -0.847 0.0
-62224 101.624 -69.165 112.331 0.46 -0.266 -0.847 0.0
-62225 101.698 -69.112 112.389 0.831 0.0 -0.556 0.0
-62226 101.615 -68.968 112.389 0.415 0.72 -0.556 0.0
-62227 101.449 -68.968 112.389 -0.415 0.72 -0.556 0.0
-62228 101.366 -69.112 112.389 -0.831 -0.0 -0.556 0.0
-62229 101.449 -69.256 112.389 -0.415 -0.72 -0.556 0.0
-62230 101.615 -69.256 112.389 0.415 -0.72 -0.556 0.0
-62231 101.697 -69.016 112.441 0.827 0.478 -0.296 0.0
-62232 101.532 -68.921 112.441 0.0 0.955 -0.296 0.0
-62233 101.367 -69.016 112.441 -0.827 0.478 -0.296 0.0
-62234 101.367 -69.208 112.441 -0.827 -0.478 -0.296 0.0
-62235 101.532 -69.303 112.441 0.0 -0.955 -0.296 0.0
-62236 101.697 -69.208 112.441 0.827 -0.478 -0.296 0.0
-62237 101.723 -69.112 112.559 0.955 0.0 0.296 0.0
-62238 101.628 -68.947 112.559 0.478 0.827 0.296 0.0
-62239 101.436 -68.947 112.559 -0.478 0.827 0.296 0.0
-62240 101.341 -69.112 112.559 -0.955 -0.0 0.296 0.0
-62241 101.436 -69.277 112.559 -0.478 -0.827 0.296 0.0
-62242 101.628 -69.277 112.559 0.478 -0.827 0.296 0.0
-62243 101.676 -69.029 112.611 0.719 0.415 0.558 0.0
-62244 101.532 -68.946 112.611 0.0 0.83 0.558 0.0
-62245 101.388 -69.029 112.611 -0.719 0.415 0.558 0.0
-62246 101.388 -69.195 112.611 -0.719 -0.415 0.558 0.0
-62247 101.532 -69.278 112.611 0.0 -0.83 0.558 0.0
-62248 101.676 -69.195 112.611 0.719 -0.415 0.558 0.0
-2009 112 8 31
-62249 101.532 -69.112 103.6 -0.0 -0.0 -1.0 0.0
-62250 101.624 -69.059 103.631 0.46 0.266 -0.847 0.0
-62251 101.532 -69.006 103.631 0.0 0.531 -0.847 0.0
-62252 101.44 -69.059 103.631 -0.46 0.266 -0.847 0.0
-62253 101.44 -69.165 103.631 -0.46 -0.266 -0.847 0.0
-62254 101.532 -69.218 103.631 0.0 -0.531 -0.847 0.0
-62255 101.624 -69.165 103.631 0.46 -0.266 -0.847 0.0
-62256 101.698 -69.112 103.689 0.831 0.0 -0.556 0.0
-62257 101.615 -68.968 103.689 0.415 0.72 -0.556 0.0
-62258 101.449 -68.968 103.689 -0.415 0.72 -0.556 0.0
-62259 101.366 -69.112 103.689 -0.831 -0.0 -0.556 0.0
-62260 101.449 -69.256 103.689 -0.415 -0.72 -0.556 0.0
-62261 101.615 -69.256 103.689 0.415 -0.72 -0.556 0.0
-62262 101.697 -69.016 103.741 0.827 0.478 -0.296 0.0
-62263 101.532 -68.921 103.741 0.0 0.955 -0.296 0.0
-62264 101.367 -69.016 103.741 -0.827 0.478 -0.296 0.0
-62265 101.367 -69.208 103.741 -0.827 -0.478 -0.296 0.0
-62266 101.532 -69.303 103.741 0.0 -0.955 -0.296 0.0
-62267 101.697 -69.208 103.741 0.827 -0.478 -0.296 0.0
-62268 101.723 -69.112 103.859 0.955 0.0 0.296 0.0
-62269 101.628 -68.947 103.859 0.478 0.827 0.296 0.0
-62270 101.436 -68.947 103.859 -0.478 0.827 0.296 0.0
-62271 101.341 -69.112 103.859 -0.955 -0.0 0.296 0.0
-62272 101.436 -69.277 103.859 -0.478 -0.827 0.296 0.0
-62273 101.628 -69.277 103.859 0.478 -0.827 0.296 0.0
-62274 101.676 -69.029 103.911 0.719 0.415 0.558 0.0
-62275 101.532 -68.946 103.911 0.0 0.83 0.558 0.0
-62276 101.388 -69.029 103.911 -0.719 0.415 0.558 0.0
-62277 101.388 -69.195 103.911 -0.719 -0.415 0.558 0.0
-62278 101.532 -69.278 103.911 0.0 -0.83 0.558 0.0
-62279 101.676 -69.195 103.911 0.719 -0.415 0.558 0.0
-2010 112 7 31
-62280 101.532 -69.112 94.2 -0.0 -0.0 -1.0 0.0
-62281 101.624 -69.059 94.231 0.46 0.266 -0.847 0.0
-62282 101.532 -69.006 94.231 0.0 0.531 -0.847 0.0
-62283 101.44 -69.059 94.231 -0.46 0.266 -0.847 0.0
-62284 101.44 -69.165 94.231 -0.46 -0.266 -0.847 0.0
-62285 101.532 -69.218 94.231 0.0 -0.531 -0.847 0.0
-62286 101.624 -69.165 94.231 0.46 -0.266 -0.847 0.0
-62287 101.698 -69.112 94.289 0.831 0.0 -0.556 0.0
-62288 101.615 -68.968 94.289 0.415 0.72 -0.556 0.0
-62289 101.449 -68.968 94.289 -0.415 0.72 -0.556 0.0
-62290 101.366 -69.112 94.289 -0.831 -0.0 -0.556 0.0
-62291 101.449 -69.256 94.289 -0.415 -0.72 -0.556 0.0
-62292 101.615 -69.256 94.289 0.415 -0.72 -0.556 0.0
-62293 101.697 -69.016 94.341 0.827 0.478 -0.296 0.0
-62294 101.532 -68.921 94.341 0.0 0.955 -0.296 0.0
-62295 101.367 -69.016 94.341 -0.827 0.478 -0.296 0.0
-62296 101.367 -69.208 94.341 -0.827 -0.478 -0.296 0.0
-62297 101.532 -69.303 94.341 0.0 -0.955 -0.296 0.0
-62298 101.697 -69.208 94.341 0.827 -0.478 -0.296 0.0
-62299 101.723 -69.112 94.459 0.955 0.0 0.296 0.0
-62300 101.628 -68.947 94.459 0.478 0.827 0.296 0.0
-62301 101.436 -68.947 94.459 -0.478 0.827 0.296 0.0
-62302 101.341 -69.112 94.459 -0.955 -0.0 0.296 0.0
-62303 101.436 -69.277 94.459 -0.478 -0.827 0.296 0.0
-62304 101.628 -69.277 94.459 0.478 -0.827 0.296 0.0
-62305 101.676 -69.029 94.511 0.719 0.415 0.558 0.0
-62306 101.532 -68.946 94.511 0.0 0.83 0.558 0.0
-62307 101.388 -69.029 94.511 -0.719 0.415 0.558 0.0
-62308 101.388 -69.195 94.511 -0.719 -0.415 0.558 0.0
-62309 101.532 -69.278 94.511 0.0 -0.83 0.558 0.0
-62310 101.676 -69.195 94.511 0.719 -0.415 0.558 0.0
-2011 112 6 31
-62311 101.532 -69.112 83.3 -0.0 -0.0 -1.0 0.0
-62312 101.624 -69.059 83.331 0.46 0.266 -0.847 0.0
-62313 101.532 -69.006 83.331 0.0 0.531 -0.847 0.0
-62314 101.44 -69.059 83.331 -0.46 0.266 -0.847 0.0
-62315 101.44 -69.165 83.331 -0.46 -0.266 -0.847 0.0
-62316 101.532 -69.218 83.331 0.0 -0.531 -0.847 0.0
-62317 101.624 -69.165 83.331 0.46 -0.266 -0.847 0.0
-62318 101.698 -69.112 83.389 0.831 0.0 -0.556 0.0
-62319 101.615 -68.968 83.389 0.415 0.72 -0.556 0.0
-62320 101.449 -68.968 83.389 -0.415 0.72 -0.556 0.0
-62321 101.366 -69.112 83.389 -0.831 -0.0 -0.556 0.0
-62322 101.449 -69.256 83.389 -0.415 -0.72 -0.556 0.0
-62323 101.615 -69.256 83.389 0.415 -0.72 -0.556 0.0
-62324 101.697 -69.016 83.441 0.827 0.478 -0.296 0.0
-62325 101.532 -68.921 83.441 0.0 0.955 -0.296 0.0
-62326 101.367 -69.016 83.441 -0.827 0.478 -0.296 0.0
-62327 101.367 -69.208 83.441 -0.827 -0.478 -0.296 0.0
-62328 101.532 -69.303 83.441 0.0 -0.955 -0.296 0.0
-62329 101.697 -69.208 83.441 0.827 -0.478 -0.296 0.0
-62330 101.723 -69.112 83.559 0.955 0.0 0.296 0.0
-62331 101.628 -68.947 83.559 0.478 0.827 0.296 0.0
-62332 101.436 -68.947 83.559 -0.478 0.827 0.296 0.0
-62333 101.341 -69.112 83.559 -0.955 -0.0 0.296 0.0
-62334 101.436 -69.277 83.559 -0.478 -0.827 0.296 0.0
-62335 101.628 -69.277 83.559 0.478 -0.827 0.296 0.0
-62336 101.676 -69.029 83.611 0.719 0.415 0.558 0.0
-62337 101.532 -68.946 83.611 0.0 0.83 0.558 0.0
-62338 101.388 -69.029 83.611 -0.719 0.415 0.558 0.0
-62339 101.388 -69.195 83.611 -0.719 -0.415 0.558 0.0
-62340 101.532 -69.278 83.611 0.0 -0.83 0.558 0.0
-62341 101.676 -69.195 83.611 0.719 -0.415 0.558 0.0
-2012 112 5 31
-62342 101.532 -69.112 73.9 -0.0 -0.0 -1.0 0.0
-62343 101.624 -69.059 73.931 0.46 0.266 -0.847 0.0
-62344 101.532 -69.006 73.931 0.0 0.531 -0.847 0.0
-62345 101.44 -69.059 73.931 -0.46 0.266 -0.847 0.0
-62346 101.44 -69.165 73.931 -0.46 -0.266 -0.847 0.0
-62347 101.532 -69.218 73.931 0.0 -0.531 -0.847 0.0
-62348 101.624 -69.165 73.931 0.46 -0.266 -0.847 0.0
-62349 101.698 -69.112 73.989 0.831 0.0 -0.556 0.0
-62350 101.615 -68.968 73.989 0.415 0.72 -0.556 0.0
-62351 101.449 -68.968 73.989 -0.415 0.72 -0.556 0.0
-62352 101.366 -69.112 73.989 -0.831 -0.0 -0.556 0.0
-62353 101.449 -69.256 73.989 -0.415 -0.72 -0.556 0.0
-62354 101.615 -69.256 73.989 0.415 -0.72 -0.556 0.0
-62355 101.697 -69.016 74.041 0.827 0.478 -0.296 0.0
-62356 101.532 -68.921 74.041 0.0 0.955 -0.296 0.0
-62357 101.367 -69.016 74.041 -0.827 0.478 -0.296 0.0
-62358 101.367 -69.208 74.041 -0.827 -0.478 -0.296 0.0
-62359 101.532 -69.303 74.041 0.0 -0.955 -0.296 0.0
-62360 101.697 -69.208 74.041 0.827 -0.478 -0.296 0.0
-62361 101.723 -69.112 74.159 0.955 0.0 0.296 0.0
-62362 101.628 -68.947 74.159 0.478 0.827 0.296 0.0
-62363 101.436 -68.947 74.159 -0.478 0.827 0.296 0.0
-62364 101.341 -69.112 74.159 -0.955 -0.0 0.296 0.0
-62365 101.436 -69.277 74.159 -0.478 -0.827 0.296 0.0
-62366 101.628 -69.277 74.159 0.478 -0.827 0.296 0.0
-62367 101.676 -69.029 74.211 0.719 0.415 0.558 0.0
-62368 101.532 -68.946 74.211 0.0 0.83 0.558 0.0
-62369 101.388 -69.029 74.211 -0.719 0.415 0.558 0.0
-62370 101.388 -69.195 74.211 -0.719 -0.415 0.558 0.0
-62371 101.532 -69.278 74.211 0.0 -0.83 0.558 0.0
-62372 101.676 -69.195 74.211 0.719 -0.415 0.558 0.0
-2013 112 4 31
-62373 101.532 -69.112 65.2 -0.0 -0.0 -1.0 0.0
-62374 101.624 -69.059 65.231 0.46 0.266 -0.847 0.0
-62375 101.532 -69.006 65.231 0.0 0.531 -0.847 0.0
-62376 101.44 -69.059 65.231 -0.46 0.266 -0.847 0.0
-62377 101.44 -69.165 65.231 -0.46 -0.266 -0.847 0.0
-62378 101.532 -69.218 65.231 0.0 -0.531 -0.847 0.0
-62379 101.624 -69.165 65.231 0.46 -0.266 -0.847 0.0
-62380 101.698 -69.112 65.289 0.831 0.0 -0.556 0.0
-62381 101.615 -68.968 65.289 0.415 0.72 -0.556 0.0
-62382 101.449 -68.968 65.289 -0.415 0.72 -0.556 0.0
-62383 101.366 -69.112 65.289 -0.831 -0.0 -0.556 0.0
-62384 101.449 -69.256 65.289 -0.415 -0.72 -0.556 0.0
-62385 101.615 -69.256 65.289 0.415 -0.72 -0.556 0.0
-62386 101.697 -69.016 65.341 0.827 0.478 -0.296 0.0
-62387 101.532 -68.921 65.341 0.0 0.955 -0.296 0.0
-62388 101.367 -69.016 65.341 -0.827 0.478 -0.296 0.0
-62389 101.367 -69.208 65.341 -0.827 -0.478 -0.296 0.0
-62390 101.532 -69.303 65.341 0.0 -0.955 -0.296 0.0
-62391 101.697 -69.208 65.341 0.827 -0.478 -0.296 0.0
-62392 101.723 -69.112 65.459 0.955 0.0 0.296 0.0
-62393 101.628 -68.947 65.459 0.478 0.827 0.296 0.0
-62394 101.436 -68.947 65.459 -0.478 0.827 0.296 0.0
-62395 101.341 -69.112 65.459 -0.955 -0.0 0.296 0.0
-62396 101.436 -69.277 65.459 -0.478 -0.827 0.296 0.0
-62397 101.628 -69.277 65.459 0.478 -0.827 0.296 0.0
-62398 101.676 -69.029 65.511 0.719 0.415 0.558 0.0
-62399 101.532 -68.946 65.511 0.0 0.83 0.558 0.0
-62400 101.388 -69.029 65.511 -0.719 0.415 0.558 0.0
-62401 101.388 -69.195 65.511 -0.719 -0.415 0.558 0.0
-62402 101.532 -69.278 65.511 0.0 -0.83 0.558 0.0
-62403 101.676 -69.195 65.511 0.719 -0.415 0.558 0.0
-2014 112 3 31
-62404 101.532 -69.112 55.8 -0.0 -0.0 -1.0 0.0
-62405 101.624 -69.059 55.831 0.46 0.266 -0.847 0.0
-62406 101.532 -69.006 55.831 0.0 0.531 -0.847 0.0
-62407 101.44 -69.059 55.831 -0.46 0.266 -0.847 0.0
-62408 101.44 -69.165 55.831 -0.46 -0.266 -0.847 0.0
-62409 101.532 -69.218 55.831 0.0 -0.531 -0.847 0.0
-62410 101.624 -69.165 55.831 0.46 -0.266 -0.847 0.0
-62411 101.698 -69.112 55.889 0.831 0.0 -0.556 0.0
-62412 101.615 -68.968 55.889 0.415 0.72 -0.556 0.0
-62413 101.449 -68.968 55.889 -0.415 0.72 -0.556 0.0
-62414 101.366 -69.112 55.889 -0.831 -0.0 -0.556 0.0
-62415 101.449 -69.256 55.889 -0.415 -0.72 -0.556 0.0
-62416 101.615 -69.256 55.889 0.415 -0.72 -0.556 0.0
-62417 101.697 -69.016 55.941 0.827 0.478 -0.296 0.0
-62418 101.532 -68.921 55.941 0.0 0.955 -0.296 0.0
-62419 101.367 -69.016 55.941 -0.827 0.478 -0.296 0.0
-62420 101.367 -69.208 55.941 -0.827 -0.478 -0.296 0.0
-62421 101.532 -69.303 55.941 0.0 -0.955 -0.296 0.0
-62422 101.697 -69.208 55.941 0.827 -0.478 -0.296 0.0
-62423 101.723 -69.112 56.059 0.955 0.0 0.296 0.0
-62424 101.628 -68.947 56.059 0.478 0.827 0.296 0.0
-62425 101.436 -68.947 56.059 -0.478 0.827 0.296 0.0
-62426 101.341 -69.112 56.059 -0.955 -0.0 0.296 0.0
-62427 101.436 -69.277 56.059 -0.478 -0.827 0.296 0.0
-62428 101.628 -69.277 56.059 0.478 -0.827 0.296 0.0
-62429 101.676 -69.029 56.111 0.719 0.415 0.558 0.0
-62430 101.532 -68.946 56.111 0.0 0.83 0.558 0.0
-62431 101.388 -69.029 56.111 -0.719 0.415 0.558 0.0
-62432 101.388 -69.195 56.111 -0.719 -0.415 0.558 0.0
-62433 101.532 -69.278 56.111 0.0 -0.83 0.558 0.0
-62434 101.676 -69.195 56.111 0.719 -0.415 0.558 0.0
-2015 112 2 31
-62435 101.532 -69.112 47.1 -0.0 -0.0 -1.0 0.0
-62436 101.624 -69.059 47.131 0.46 0.266 -0.847 0.0
-62437 101.532 -69.006 47.131 0.0 0.531 -0.847 0.0
-62438 101.44 -69.059 47.131 -0.46 0.266 -0.847 0.0
-62439 101.44 -69.165 47.131 -0.46 -0.266 -0.847 0.0
-62440 101.532 -69.218 47.131 0.0 -0.531 -0.847 0.0
-62441 101.624 -69.165 47.131 0.46 -0.266 -0.847 0.0
-62442 101.698 -69.112 47.189 0.831 0.0 -0.556 0.0
-62443 101.615 -68.968 47.189 0.415 0.72 -0.556 0.0
-62444 101.449 -68.968 47.189 -0.415 0.72 -0.556 0.0
-62445 101.366 -69.112 47.189 -0.831 -0.0 -0.556 0.0
-62446 101.449 -69.256 47.189 -0.415 -0.72 -0.556 0.0
-62447 101.615 -69.256 47.189 0.415 -0.72 -0.556 0.0
-62448 101.697 -69.016 47.241 0.827 0.478 -0.296 0.0
-62449 101.532 -68.921 47.241 0.0 0.955 -0.296 0.0
-62450 101.367 -69.016 47.241 -0.827 0.478 -0.296 0.0
-62451 101.367 -69.208 47.241 -0.827 -0.478 -0.296 0.0
-62452 101.532 -69.303 47.241 0.0 -0.955 -0.296 0.0
-62453 101.697 -69.208 47.241 0.827 -0.478 -0.296 0.0
-62454 101.723 -69.112 47.359 0.955 0.0 0.296 0.0
-62455 101.628 -68.947 47.359 0.478 0.827 0.296 0.0
-62456 101.436 -68.947 47.359 -0.478 0.827 0.296 0.0
-62457 101.341 -69.112 47.359 -0.955 -0.0 0.296 0.0
-62458 101.436 -69.277 47.359 -0.478 -0.827 0.296 0.0
-62459 101.628 -69.277 47.359 0.478 -0.827 0.296 0.0
-62460 101.676 -69.029 47.411 0.719 0.415 0.558 0.0
-62461 101.532 -68.946 47.411 0.0 0.83 0.558 0.0
-62462 101.388 -69.029 47.411 -0.719 0.415 0.558 0.0
-62463 101.388 -69.195 47.411 -0.719 -0.415 0.558 0.0
-62464 101.532 -69.278 47.411 0.0 -0.83 0.558 0.0
-62465 101.676 -69.195 47.411 0.719 -0.415 0.558 0.0
-2016 112 1 31
-62466 101.532 -69.112 37.7 -0.0 -0.0 -1.0 0.0
-62467 101.624 -69.059 37.731 0.46 0.266 -0.847 0.0
-62468 101.532 -69.006 37.731 0.0 0.531 -0.847 0.0
-62469 101.44 -69.059 37.731 -0.46 0.266 -0.847 0.0
-62470 101.44 -69.165 37.731 -0.46 -0.266 -0.847 0.0
-62471 101.532 -69.218 37.731 0.0 -0.531 -0.847 0.0
-62472 101.624 -69.165 37.731 0.46 -0.266 -0.847 0.0
-62473 101.698 -69.112 37.789 0.831 0.0 -0.556 0.0
-62474 101.615 -68.968 37.789 0.415 0.72 -0.556 0.0
-62475 101.449 -68.968 37.789 -0.415 0.72 -0.556 0.0
-62476 101.366 -69.112 37.789 -0.831 -0.0 -0.556 0.0
-62477 101.449 -69.256 37.789 -0.415 -0.72 -0.556 0.0
-62478 101.615 -69.256 37.789 0.415 -0.72 -0.556 0.0
-62479 101.697 -69.016 37.841 0.827 0.478 -0.296 0.0
-62480 101.532 -68.921 37.841 0.0 0.955 -0.296 0.0
-62481 101.367 -69.016 37.841 -0.827 0.478 -0.296 0.0
-62482 101.367 -69.208 37.841 -0.827 -0.478 -0.296 0.0
-62483 101.532 -69.303 37.841 0.0 -0.955 -0.296 0.0
-62484 101.697 -69.208 37.841 0.827 -0.478 -0.296 0.0
-62485 101.723 -69.112 37.959 0.955 0.0 0.296 0.0
-62486 101.628 -68.947 37.959 0.478 0.827 0.296 0.0
-62487 101.436 -68.947 37.959 -0.478 0.827 0.296 0.0
-62488 101.341 -69.112 37.959 -0.955 -0.0 0.296 0.0
-62489 101.436 -69.277 37.959 -0.478 -0.827 0.296 0.0
-62490 101.628 -69.277 37.959 0.478 -0.827 0.296 0.0
-62491 101.676 -69.029 38.011 0.719 0.415 0.558 0.0
-62492 101.532 -68.946 38.011 0.0 0.83 0.558 0.0
-62493 101.388 -69.029 38.011 -0.719 0.415 0.558 0.0
-62494 101.388 -69.195 38.011 -0.719 -0.415 0.558 0.0
-62495 101.532 -69.278 38.011 0.0 -0.83 0.558 0.0
-62496 101.676 -69.195 38.011 0.719 -0.415 0.558 0.0
-2017 113 18 31
-62497 46.304 -88.668 196.3 -0.0 -0.0 -1.0 0.0
-62498 46.396 -88.615 196.331 0.46 0.266 -0.847 0.0
-62499 46.304 -88.562 196.331 0.0 0.531 -0.847 0.0
-62500 46.212 -88.615 196.331 -0.46 0.266 -0.847 0.0
-62501 46.212 -88.721 196.331 -0.46 -0.266 -0.847 0.0
-62502 46.304 -88.774 196.331 0.0 -0.531 -0.847 0.0
-62503 46.396 -88.721 196.331 0.46 -0.266 -0.847 0.0
-62504 46.47 -88.668 196.389 0.831 0.0 -0.556 0.0
-62505 46.387 -88.524 196.389 0.415 0.72 -0.556 0.0
-62506 46.221 -88.524 196.389 -0.415 0.72 -0.556 0.0
-62507 46.138 -88.668 196.389 -0.831 -0.0 -0.556 0.0
-62508 46.221 -88.812 196.389 -0.415 -0.72 -0.556 0.0
-62509 46.387 -88.812 196.389 0.415 -0.72 -0.556 0.0
-62510 46.469 -88.572 196.441 0.827 0.478 -0.296 0.0
-62511 46.304 -88.477 196.441 0.0 0.955 -0.296 0.0
-62512 46.139 -88.572 196.441 -0.827 0.478 -0.296 0.0
-62513 46.139 -88.764 196.441 -0.827 -0.478 -0.296 0.0
-62514 46.304 -88.859 196.441 0.0 -0.955 -0.296 0.0
-62515 46.469 -88.764 196.441 0.827 -0.478 -0.296 0.0
-62516 46.495 -88.668 196.559 0.955 0.0 0.296 0.0
-62517 46.4 -88.503 196.559 0.478 0.827 0.296 0.0
-62518 46.208 -88.503 196.559 -0.478 0.827 0.296 0.0
-62519 46.113 -88.668 196.559 -0.955 -0.0 0.296 0.0
-62520 46.208 -88.833 196.559 -0.478 -0.827 0.296 0.0
-62521 46.4 -88.833 196.559 0.478 -0.827 0.296 0.0
-62522 46.448 -88.585 196.611 0.719 0.415 0.558 0.0
-62523 46.304 -88.502 196.611 0.0 0.83 0.558 0.0
-62524 46.16 -88.585 196.611 -0.719 0.415 0.558 0.0
-62525 46.16 -88.751 196.611 -0.719 -0.415 0.558 0.0
-62526 46.304 -88.834 196.611 0.0 -0.83 0.558 0.0
-62527 46.448 -88.751 196.611 0.719 -0.415 0.558 0.0
-2018 113 17 31
-62528 46.304 -88.668 186.9 -0.0 -0.0 -1.0 0.0
-62529 46.396 -88.615 186.931 0.46 0.266 -0.847 0.0
-62530 46.304 -88.562 186.931 0.0 0.531 -0.847 0.0
-62531 46.212 -88.615 186.931 -0.46 0.266 -0.847 0.0
-62532 46.212 -88.721 186.931 -0.46 -0.266 -0.847 0.0
-62533 46.304 -88.774 186.931 0.0 -0.531 -0.847 0.0
-62534 46.396 -88.721 186.931 0.46 -0.266 -0.847 0.0
-62535 46.47 -88.668 186.989 0.831 0.0 -0.556 0.0
-62536 46.387 -88.524 186.989 0.415 0.72 -0.556 0.0
-62537 46.221 -88.524 186.989 -0.415 0.72 -0.556 0.0
-62538 46.138 -88.668 186.989 -0.831 -0.0 -0.556 0.0
-62539 46.221 -88.812 186.989 -0.415 -0.72 -0.556 0.0
-62540 46.387 -88.812 186.989 0.415 -0.72 -0.556 0.0
-62541 46.469 -88.572 187.041 0.827 0.478 -0.296 0.0
-62542 46.304 -88.477 187.041 0.0 0.955 -0.296 0.0
-62543 46.139 -88.572 187.041 -0.827 0.478 -0.296 0.0
-62544 46.139 -88.764 187.041 -0.827 -0.478 -0.296 0.0
-62545 46.304 -88.859 187.041 0.0 -0.955 -0.296 0.0
-62546 46.469 -88.764 187.041 0.827 -0.478 -0.296 0.0
-62547 46.495 -88.668 187.159 0.955 0.0 0.296 0.0
-62548 46.4 -88.503 187.159 0.478 0.827 0.296 0.0
-62549 46.208 -88.503 187.159 -0.478 0.827 0.296 0.0
-62550 46.113 -88.668 187.159 -0.955 -0.0 0.296 0.0
-62551 46.208 -88.833 187.159 -0.478 -0.827 0.296 0.0
-62552 46.4 -88.833 187.159 0.478 -0.827 0.296 0.0
-62553 46.448 -88.585 187.211 0.719 0.415 0.558 0.0
-62554 46.304 -88.502 187.211 0.0 0.83 0.558 0.0
-62555 46.16 -88.585 187.211 -0.719 0.415 0.558 0.0
-62556 46.16 -88.751 187.211 -0.719 -0.415 0.558 0.0
-62557 46.304 -88.834 187.211 0.0 -0.83 0.558 0.0
-62558 46.448 -88.751 187.211 0.719 -0.415 0.558 0.0
-2019 113 16 31
-62559 46.304 -88.668 178.2 -0.0 -0.0 -1.0 0.0
-62560 46.396 -88.615 178.231 0.46 0.266 -0.847 0.0
-62561 46.304 -88.562 178.231 0.0 0.531 -0.847 0.0
-62562 46.212 -88.615 178.231 -0.46 0.266 -0.847 0.0
-62563 46.212 -88.721 178.231 -0.46 -0.266 -0.847 0.0
-62564 46.304 -88.774 178.231 0.0 -0.531 -0.847 0.0
-62565 46.396 -88.721 178.231 0.46 -0.266 -0.847 0.0
-62566 46.47 -88.668 178.289 0.831 0.0 -0.556 0.0
-62567 46.387 -88.524 178.289 0.415 0.72 -0.556 0.0
-62568 46.221 -88.524 178.289 -0.415 0.72 -0.556 0.0
-62569 46.138 -88.668 178.289 -0.831 -0.0 -0.556 0.0
-62570 46.221 -88.812 178.289 -0.415 -0.72 -0.556 0.0
-62571 46.387 -88.812 178.289 0.415 -0.72 -0.556 0.0
-62572 46.469 -88.572 178.341 0.827 0.478 -0.296 0.0
-62573 46.304 -88.477 178.341 0.0 0.955 -0.296 0.0
-62574 46.139 -88.572 178.341 -0.827 0.478 -0.296 0.0
-62575 46.139 -88.764 178.341 -0.827 -0.478 -0.296 0.0
-62576 46.304 -88.859 178.341 0.0 -0.955 -0.296 0.0
-62577 46.469 -88.764 178.341 0.827 -0.478 -0.296 0.0
-62578 46.495 -88.668 178.459 0.955 0.0 0.296 0.0
-62579 46.4 -88.503 178.459 0.478 0.827 0.296 0.0
-62580 46.208 -88.503 178.459 -0.478 0.827 0.296 0.0
-62581 46.113 -88.668 178.459 -0.955 -0.0 0.296 0.0
-62582 46.208 -88.833 178.459 -0.478 -0.827 0.296 0.0
-62583 46.4 -88.833 178.459 0.478 -0.827 0.296 0.0
-62584 46.448 -88.585 178.511 0.719 0.415 0.558 0.0
-62585 46.304 -88.502 178.511 0.0 0.83 0.558 0.0
-62586 46.16 -88.585 178.511 -0.719 0.415 0.558 0.0
-62587 46.16 -88.751 178.511 -0.719 -0.415 0.558 0.0
-62588 46.304 -88.834 178.511 0.0 -0.83 0.558 0.0
-62589 46.448 -88.751 178.511 0.719 -0.415 0.558 0.0
-2020 113 15 31
-62590 46.304 -88.668 168.8 -0.0 -0.0 -1.0 0.0
-62591 46.396 -88.615 168.831 0.46 0.266 -0.847 0.0
-62592 46.304 -88.562 168.831 0.0 0.531 -0.847 0.0
-62593 46.212 -88.615 168.831 -0.46 0.266 -0.847 0.0
-62594 46.212 -88.721 168.831 -0.46 -0.266 -0.847 0.0
-62595 46.304 -88.774 168.831 0.0 -0.531 -0.847 0.0
-62596 46.396 -88.721 168.831 0.46 -0.266 -0.847 0.0
-62597 46.47 -88.668 168.889 0.831 0.0 -0.556 0.0
-62598 46.387 -88.524 168.889 0.415 0.72 -0.556 0.0
-62599 46.221 -88.524 168.889 -0.415 0.72 -0.556 0.0
-62600 46.138 -88.668 168.889 -0.831 -0.0 -0.556 0.0
-62601 46.221 -88.812 168.889 -0.415 -0.72 -0.556 0.0
-62602 46.387 -88.812 168.889 0.415 -0.72 -0.556 0.0
-62603 46.469 -88.572 168.941 0.827 0.478 -0.296 0.0
-62604 46.304 -88.477 168.941 0.0 0.955 -0.296 0.0
-62605 46.139 -88.572 168.941 -0.827 0.478 -0.296 0.0
-62606 46.139 -88.764 168.941 -0.827 -0.478 -0.296 0.0
-62607 46.304 -88.859 168.941 0.0 -0.955 -0.296 0.0
-62608 46.469 -88.764 168.941 0.827 -0.478 -0.296 0.0
-62609 46.495 -88.668 169.059 0.955 0.0 0.296 0.0
-62610 46.4 -88.503 169.059 0.478 0.827 0.296 0.0
-62611 46.208 -88.503 169.059 -0.478 0.827 0.296 0.0
-62612 46.113 -88.668 169.059 -0.955 -0.0 0.296 0.0
-62613 46.208 -88.833 169.059 -0.478 -0.827 0.296 0.0
-62614 46.4 -88.833 169.059 0.478 -0.827 0.296 0.0
-62615 46.448 -88.585 169.111 0.719 0.415 0.558 0.0
-62616 46.304 -88.502 169.111 0.0 0.83 0.558 0.0
-62617 46.16 -88.585 169.111 -0.719 0.415 0.558 0.0
-62618 46.16 -88.751 169.111 -0.719 -0.415 0.558 0.0
-62619 46.304 -88.834 169.111 0.0 -0.83 0.558 0.0
-62620 46.448 -88.751 169.111 0.719 -0.415 0.558 0.0
-2021 113 14 31
-62621 46.304 -88.668 160.1 -0.0 -0.0 -1.0 0.0
-62622 46.396 -88.615 160.131 0.46 0.266 -0.847 0.0
-62623 46.304 -88.562 160.131 0.0 0.531 -0.847 0.0
-62624 46.212 -88.615 160.131 -0.46 0.266 -0.847 0.0
-62625 46.212 -88.721 160.131 -0.46 -0.266 -0.847 0.0
-62626 46.304 -88.774 160.131 0.0 -0.531 -0.847 0.0
-62627 46.396 -88.721 160.131 0.46 -0.266 -0.847 0.0
-62628 46.47 -88.668 160.189 0.831 0.0 -0.556 0.0
-62629 46.387 -88.524 160.189 0.415 0.72 -0.556 0.0
-62630 46.221 -88.524 160.189 -0.415 0.72 -0.556 0.0
-62631 46.138 -88.668 160.189 -0.831 -0.0 -0.556 0.0
-62632 46.221 -88.812 160.189 -0.415 -0.72 -0.556 0.0
-62633 46.387 -88.812 160.189 0.415 -0.72 -0.556 0.0
-62634 46.469 -88.572 160.241 0.827 0.478 -0.296 0.0
-62635 46.304 -88.477 160.241 0.0 0.955 -0.296 0.0
-62636 46.139 -88.572 160.241 -0.827 0.478 -0.296 0.0
-62637 46.139 -88.764 160.241 -0.827 -0.478 -0.296 0.0
-62638 46.304 -88.859 160.241 0.0 -0.955 -0.296 0.0
-62639 46.469 -88.764 160.241 0.827 -0.478 -0.296 0.0
-62640 46.495 -88.668 160.359 0.955 0.0 0.296 0.0
-62641 46.4 -88.503 160.359 0.478 0.827 0.296 0.0
-62642 46.208 -88.503 160.359 -0.478 0.827 0.296 0.0
-62643 46.113 -88.668 160.359 -0.955 -0.0 0.296 0.0
-62644 46.208 -88.833 160.359 -0.478 -0.827 0.296 0.0
-62645 46.4 -88.833 160.359 0.478 -0.827 0.296 0.0
-62646 46.448 -88.585 160.411 0.719 0.415 0.558 0.0
-62647 46.304 -88.502 160.411 0.0 0.83 0.558 0.0
-62648 46.16 -88.585 160.411 -0.719 0.415 0.558 0.0
-62649 46.16 -88.751 160.411 -0.719 -0.415 0.558 0.0
-62650 46.304 -88.834 160.411 0.0 -0.83 0.558 0.0
-62651 46.448 -88.751 160.411 0.719 -0.415 0.558 0.0
-2022 113 13 31
-62652 46.304 -88.668 150.7 -0.0 -0.0 -1.0 0.0
-62653 46.396 -88.615 150.731 0.46 0.266 -0.847 0.0
-62654 46.304 -88.562 150.731 0.0 0.531 -0.847 0.0
-62655 46.212 -88.615 150.731 -0.46 0.266 -0.847 0.0
-62656 46.212 -88.721 150.731 -0.46 -0.266 -0.847 0.0
-62657 46.304 -88.774 150.731 0.0 -0.531 -0.847 0.0
-62658 46.396 -88.721 150.731 0.46 -0.266 -0.847 0.0
-62659 46.47 -88.668 150.789 0.831 0.0 -0.556 0.0
-62660 46.387 -88.524 150.789 0.415 0.72 -0.556 0.0
-62661 46.221 -88.524 150.789 -0.415 0.72 -0.556 0.0
-62662 46.138 -88.668 150.789 -0.831 -0.0 -0.556 0.0
-62663 46.221 -88.812 150.789 -0.415 -0.72 -0.556 0.0
-62664 46.387 -88.812 150.789 0.415 -0.72 -0.556 0.0
-62665 46.469 -88.572 150.841 0.827 0.478 -0.296 0.0
-62666 46.304 -88.477 150.841 0.0 0.955 -0.296 0.0
-62667 46.139 -88.572 150.841 -0.827 0.478 -0.296 0.0
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-64031 88.095 -88.592 74.041 -0.827 0.478 -0.296 0.0
-64032 88.095 -88.784 74.041 -0.827 -0.478 -0.296 0.0
-64033 88.26 -88.879 74.041 0.0 -0.955 -0.296 0.0
-64034 88.425 -88.784 74.041 0.827 -0.478 -0.296 0.0
-64035 88.451 -88.688 74.159 0.955 0.0 0.296 0.0
-64036 88.356 -88.523 74.159 0.478 0.827 0.296 0.0
-64037 88.164 -88.523 74.159 -0.478 0.827 0.296 0.0
-64038 88.069 -88.688 74.159 -0.955 -0.0 0.296 0.0
-64039 88.164 -88.853 74.159 -0.478 -0.827 0.296 0.0
-64040 88.356 -88.853 74.159 0.478 -0.827 0.296 0.0
-64041 88.404 -88.605 74.211 0.719 0.415 0.558 0.0
-64042 88.26 -88.522 74.211 0.0 0.83 0.558 0.0
-64043 88.116 -88.605 74.211 -0.719 0.415 0.558 0.0
-64044 88.116 -88.771 74.211 -0.719 -0.415 0.558 0.0
-64045 88.26 -88.854 74.211 0.0 -0.83 0.558 0.0
-64046 88.404 -88.771 74.211 0.719 -0.415 0.558 0.0
-2067 115 4 31
-64047 88.26 -88.688 65.2 -0.0 -0.0 -1.0 0.0
-64048 88.352 -88.635 65.231 0.46 0.266 -0.847 0.0
-64049 88.26 -88.582 65.231 0.0 0.531 -0.847 0.0
-64050 88.168 -88.635 65.231 -0.46 0.266 -0.847 0.0
-64051 88.168 -88.741 65.231 -0.46 -0.266 -0.847 0.0
-64052 88.26 -88.794 65.231 0.0 -0.531 -0.847 0.0
-64053 88.352 -88.741 65.231 0.46 -0.266 -0.847 0.0
-64054 88.426 -88.688 65.289 0.831 0.0 -0.556 0.0
-64055 88.343 -88.544 65.289 0.415 0.72 -0.556 0.0
-64056 88.177 -88.544 65.289 -0.415 0.72 -0.556 0.0
-64057 88.094 -88.688 65.289 -0.831 -0.0 -0.556 0.0
-64058 88.177 -88.832 65.289 -0.415 -0.72 -0.556 0.0
-64059 88.343 -88.832 65.289 0.415 -0.72 -0.556 0.0
-64060 88.425 -88.592 65.341 0.827 0.478 -0.296 0.0
-64061 88.26 -88.497 65.341 0.0 0.955 -0.296 0.0
-64062 88.095 -88.592 65.341 -0.827 0.478 -0.296 0.0
-64063 88.095 -88.784 65.341 -0.827 -0.478 -0.296 0.0
-64064 88.26 -88.879 65.341 0.0 -0.955 -0.296 0.0
-64065 88.425 -88.784 65.341 0.827 -0.478 -0.296 0.0
-64066 88.451 -88.688 65.459 0.955 0.0 0.296 0.0
-64067 88.356 -88.523 65.459 0.478 0.827 0.296 0.0
-64068 88.164 -88.523 65.459 -0.478 0.827 0.296 0.0
-64069 88.069 -88.688 65.459 -0.955 -0.0 0.296 0.0
-64070 88.164 -88.853 65.459 -0.478 -0.827 0.296 0.0
-64071 88.356 -88.853 65.459 0.478 -0.827 0.296 0.0
-64072 88.404 -88.605 65.511 0.719 0.415 0.558 0.0
-64073 88.26 -88.522 65.511 0.0 0.83 0.558 0.0
-64074 88.116 -88.605 65.511 -0.719 0.415 0.558 0.0
-64075 88.116 -88.771 65.511 -0.719 -0.415 0.558 0.0
-64076 88.26 -88.854 65.511 0.0 -0.83 0.558 0.0
-64077 88.404 -88.771 65.511 0.719 -0.415 0.558 0.0
-2068 115 3 31
-64078 88.26 -88.688 55.8 -0.0 -0.0 -1.0 0.0
-64079 88.352 -88.635 55.831 0.46 0.266 -0.847 0.0
-64080 88.26 -88.582 55.831 0.0 0.531 -0.847 0.0
-64081 88.168 -88.635 55.831 -0.46 0.266 -0.847 0.0
-64082 88.168 -88.741 55.831 -0.46 -0.266 -0.847 0.0
-64083 88.26 -88.794 55.831 0.0 -0.531 -0.847 0.0
-64084 88.352 -88.741 55.831 0.46 -0.266 -0.847 0.0
-64085 88.426 -88.688 55.889 0.831 0.0 -0.556 0.0
-64086 88.343 -88.544 55.889 0.415 0.72 -0.556 0.0
-64087 88.177 -88.544 55.889 -0.415 0.72 -0.556 0.0
-64088 88.094 -88.688 55.889 -0.831 -0.0 -0.556 0.0
-64089 88.177 -88.832 55.889 -0.415 -0.72 -0.556 0.0
-64090 88.343 -88.832 55.889 0.415 -0.72 -0.556 0.0
-64091 88.425 -88.592 55.941 0.827 0.478 -0.296 0.0
-64092 88.26 -88.497 55.941 0.0 0.955 -0.296 0.0
-64093 88.095 -88.592 55.941 -0.827 0.478 -0.296 0.0
-64094 88.095 -88.784 55.941 -0.827 -0.478 -0.296 0.0
-64095 88.26 -88.879 55.941 0.0 -0.955 -0.296 0.0
-64096 88.425 -88.784 55.941 0.827 -0.478 -0.296 0.0
-64097 88.451 -88.688 56.059 0.955 0.0 0.296 0.0
-64098 88.356 -88.523 56.059 0.478 0.827 0.296 0.0
-64099 88.164 -88.523 56.059 -0.478 0.827 0.296 0.0
-64100 88.069 -88.688 56.059 -0.955 -0.0 0.296 0.0
-64101 88.164 -88.853 56.059 -0.478 -0.827 0.296 0.0
-64102 88.356 -88.853 56.059 0.478 -0.827 0.296 0.0
-64103 88.404 -88.605 56.111 0.719 0.415 0.558 0.0
-64104 88.26 -88.522 56.111 0.0 0.83 0.558 0.0
-64105 88.116 -88.605 56.111 -0.719 0.415 0.558 0.0
-64106 88.116 -88.771 56.111 -0.719 -0.415 0.558 0.0
-64107 88.26 -88.854 56.111 0.0 -0.83 0.558 0.0
-64108 88.404 -88.771 56.111 0.719 -0.415 0.558 0.0
-2069 115 2 31
-64109 88.26 -88.688 47.1 -0.0 -0.0 -1.0 0.0
-64110 88.352 -88.635 47.131 0.46 0.266 -0.847 0.0
-64111 88.26 -88.582 47.131 0.0 0.531 -0.847 0.0
-64112 88.168 -88.635 47.131 -0.46 0.266 -0.847 0.0
-64113 88.168 -88.741 47.131 -0.46 -0.266 -0.847 0.0
-64114 88.26 -88.794 47.131 0.0 -0.531 -0.847 0.0
-64115 88.352 -88.741 47.131 0.46 -0.266 -0.847 0.0
-64116 88.426 -88.688 47.189 0.831 0.0 -0.556 0.0
-64117 88.343 -88.544 47.189 0.415 0.72 -0.556 0.0
-64118 88.177 -88.544 47.189 -0.415 0.72 -0.556 0.0
-64119 88.094 -88.688 47.189 -0.831 -0.0 -0.556 0.0
-64120 88.177 -88.832 47.189 -0.415 -0.72 -0.556 0.0
-64121 88.343 -88.832 47.189 0.415 -0.72 -0.556 0.0
-64122 88.425 -88.592 47.241 0.827 0.478 -0.296 0.0
-64123 88.26 -88.497 47.241 0.0 0.955 -0.296 0.0
-64124 88.095 -88.592 47.241 -0.827 0.478 -0.296 0.0
-64125 88.095 -88.784 47.241 -0.827 -0.478 -0.296 0.0
-64126 88.26 -88.879 47.241 0.0 -0.955 -0.296 0.0
-64127 88.425 -88.784 47.241 0.827 -0.478 -0.296 0.0
-64128 88.451 -88.688 47.359 0.955 0.0 0.296 0.0
-64129 88.356 -88.523 47.359 0.478 0.827 0.296 0.0
-64130 88.164 -88.523 47.359 -0.478 0.827 0.296 0.0
-64131 88.069 -88.688 47.359 -0.955 -0.0 0.296 0.0
-64132 88.164 -88.853 47.359 -0.478 -0.827 0.296 0.0
-64133 88.356 -88.853 47.359 0.478 -0.827 0.296 0.0
-64134 88.404 -88.605 47.411 0.719 0.415 0.558 0.0
-64135 88.26 -88.522 47.411 0.0 0.83 0.558 0.0
-64136 88.116 -88.605 47.411 -0.719 0.415 0.558 0.0
-64137 88.116 -88.771 47.411 -0.719 -0.415 0.558 0.0
-64138 88.26 -88.854 47.411 0.0 -0.83 0.558 0.0
-64139 88.404 -88.771 47.411 0.719 -0.415 0.558 0.0
-2070 115 1 31
-64140 88.26 -88.688 37.7 -0.0 -0.0 -1.0 0.0
-64141 88.352 -88.635 37.731 0.46 0.266 -0.847 0.0
-64142 88.26 -88.582 37.731 0.0 0.531 -0.847 0.0
-64143 88.168 -88.635 37.731 -0.46 0.266 -0.847 0.0
-64144 88.168 -88.741 37.731 -0.46 -0.266 -0.847 0.0
-64145 88.26 -88.794 37.731 0.0 -0.531 -0.847 0.0
-64146 88.352 -88.741 37.731 0.46 -0.266 -0.847 0.0
-64147 88.426 -88.688 37.789 0.831 0.0 -0.556 0.0
-64148 88.343 -88.544 37.789 0.415 0.72 -0.556 0.0
-64149 88.177 -88.544 37.789 -0.415 0.72 -0.556 0.0
-64150 88.094 -88.688 37.789 -0.831 -0.0 -0.556 0.0
-64151 88.177 -88.832 37.789 -0.415 -0.72 -0.556 0.0
-64152 88.343 -88.832 37.789 0.415 -0.72 -0.556 0.0
-64153 88.425 -88.592 37.841 0.827 0.478 -0.296 0.0
-64154 88.26 -88.497 37.841 0.0 0.955 -0.296 0.0
-64155 88.095 -88.592 37.841 -0.827 0.478 -0.296 0.0
-64156 88.095 -88.784 37.841 -0.827 -0.478 -0.296 0.0
-64157 88.26 -88.879 37.841 0.0 -0.955 -0.296 0.0
-64158 88.425 -88.784 37.841 0.827 -0.478 -0.296 0.0
-64159 88.451 -88.688 37.959 0.955 0.0 0.296 0.0
-64160 88.356 -88.523 37.959 0.478 0.827 0.296 0.0
-64161 88.164 -88.523 37.959 -0.478 0.827 0.296 0.0
-64162 88.069 -88.688 37.959 -0.955 -0.0 0.296 0.0
-64163 88.164 -88.853 37.959 -0.478 -0.827 0.296 0.0
-64164 88.356 -88.853 37.959 0.478 -0.827 0.296 0.0
-64165 88.404 -88.605 38.011 0.719 0.415 0.558 0.0
-64166 88.26 -88.522 38.011 0.0 0.83 0.558 0.0
-64167 88.116 -88.605 38.011 -0.719 0.415 0.558 0.0
-64168 88.116 -88.771 38.011 -0.719 -0.415 0.558 0.0
-64169 88.26 -88.854 38.011 0.0 -0.83 0.558 0.0
-64170 88.404 -88.771 38.011 0.719 -0.415 0.558 0.0
diff --git a/setup.py b/setup.py
index 100ea4c680088b19837952c19fe7722c0d91447e..e7c332088b59198ce2b17a74e404d3e8b8bb0eb4 100644
--- a/setup.py
+++ b/setup.py
@@ -30,9 +30,9 @@ setup(
'concatenate=orcasong.tools.concatenate:main',
'h5shuffle=orcasong.tools.postproc:h5shuffle',
'h5shuffle2=orcasong.tools.shuffle2:run_parser',
+ 'make_dsplit=orcasong.tools.make_data_split:main',
'plot_binstats=orcasong.plotting.plot_binstats:main',
- 'make_nn_images=legacy.make_nn_images:main',
- 'make_dsplit=orcasong_contrib.data_tools.make_data_split.make_data_split:main']}
+ ]}
)
__author__ = 'Stefan Reck, Michael Moser, Daniel Guderian'
diff --git a/tests/data/processed_data_muon/processed_graph_muon.h5 b/tests/data/processed_data_muon/processed_graph_muon.h5
new file mode 100644
index 0000000000000000000000000000000000000000..4dedba36e47d6e75288f30711f254151a2370203
Binary files /dev/null and b/tests/data/processed_data_muon/processed_graph_muon.h5 differ
diff --git a/tests/data/processed_data_neutrino/processed_graph_neutrino.h5 b/tests/data/processed_data_neutrino/processed_graph_neutrino.h5
new file mode 100644
index 0000000000000000000000000000000000000000..136b305d4e49ccbf590b55023e75d30b0d758e6a
Binary files /dev/null and b/tests/data/processed_data_neutrino/processed_graph_neutrino.h5 differ
diff --git a/tests/data/test_make_data_split_config.toml b/tests/data/test_make_data_split_config.toml
new file mode 100644
index 0000000000000000000000000000000000000000..dc6db86366c7c7e60b4b022069ccd461d8edb611
--- /dev/null
+++ b/tests/data/test_make_data_split_config.toml
@@ -0,0 +1,75 @@
+# Example configuration file for make_data_split.py
+
+# --- Documentation for every config parameter that is available --- #
+#
+# Main Parameters
+# ----------
+# n_files_train : int
+# Into how many files the training dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# n_files_validate : int
+# Into how many files the validation dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# n_files_rest : int
+# Into how many files the "rest" dataset should be split.
+# If you don't want to have this dataset, comment out the line or delete it!
+# output_file_folder : str
+# Path to the folder, where all the output .list files (and the bash job scripts) should be stored.
+# output_file_name : str
+# String, that specifies the prefix of the filename of the output .list files.
+# E.g. if = "xyzc_tight_0":
+# xyzc_tight_0_train_0.list, xyzc_tight_0_validate_0.list, ...
+# print_only : bool
+# If only informationa about the input_groups should be printed, and no .list files should be made.
+#
+#
+# Input Group Parameters
+# ----------------------
+# dir : str
+# Path of the directory, where the files for this input group are located.
+# run_ids_train/run_ids_validate/run_ids_rest : array
+# Array, which specifies the range of the run_ids, that should be used for the training/validation.rest
+# dataset of this input group.
+# E.g. if [1,5], the script will put files from this input group with run_ids from 1 to 5 (including 1 and 5)
+# to the training/validation/rest dataset.
+# If you don't want to use a specific dataset for this input group, comment out the line or delete it!
+#
+# --- Documentation for every config parameter that is available --- #
+
+# --- Main options ---#
+
+n_files_train = 1
+n_files_validate = 1
+n_files_rest = 0
+
+output_file_folder = "data_split_test_output"
+
+output_file_name = "test_list"
+
+print_only = false # only print information of your input_groups, don't make any .list files
+
+make_qsub_bash_files = true
+
+venv_path = "/sps/km3net/users/guderian/NN_stuff/deep_learning_source/venv_song/bin/"
+
+shuffle_delete = false
+
+# --- Main options ---#
+
+
+# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
+
+
+[neutrino]
+dir = "processed_data_neutrino"
+run_ids_train = [1, 6767]
+run_ids_validate = [1, 6769]
+
+
+[muon]
+dir = "processed_data_muon"
+run_ids_train = [1, 6767]
+run_ids_validate = [9999, 6769]
+
+
+# --- Input groups : these are the datafiles, that should be concatenated somehow --- #
\ No newline at end of file
diff --git a/tests/test_concatenate.py b/tests/test_concatenate.py
index 47a4b4292483da5e3d6e6746a7847e1c712c9c75..60f39cb6710daf5461e66d4b561ee79b9b56f837 100644
--- a/tests/test_concatenate.py
+++ b/tests/test_concatenate.py
@@ -9,7 +9,7 @@ __author__ = 'Stefan Reck'
class TestFileConcatenator(TestCase):
"""
- Test concatenation on pre-generated h5 files. They are in test/data.
+ Test concatenation on pre-generated h5 files. They are in tests/data.
"""
@classmethod
diff --git a/tests/test_extractor.py b/tests/test_extractor.py
index 7e6320de1966128d898b3ff0b112af44e2520137..66d65e8ed9a64187e85b57b2b3ac77297aeda5c6 100644
--- a/tests/test_extractor.py
+++ b/tests/test_extractor.py
@@ -16,7 +16,7 @@ DET_FILE_NEUTRINO = os.path.join(test_dir, "data", "neutrino_detector_file.detx"
class TestStdRecoExtractor(TestCase):
- """ Assert that the neutrino info is extracted correctly File has 18 events. """
+ """ Assert that the neutrino info is extracted correctly. File has 18 events. """
@classmethod
def setUpClass(cls):
@@ -33,7 +33,7 @@ class TestStdRecoExtractor(TestCase):
cls.outfile = os.path.join(cls.tmpdir.name, "binned.h5")
cls.proc.run(infile=NEUTRINO_FILE, outfile=cls.outfile)
cls.f = h5py.File(cls.outfile, "r")
-
+
@classmethod
def tearDownClass(cls):
cls.f.close()
@@ -192,3 +192,5 @@ class TestStdRecoExtractor(TestCase):
}
for k, v in target.items():
np.testing.assert_equal(y[k], v)
+
+
\ No newline at end of file
diff --git a/tests/test_make_data_split.py b/tests/test_make_data_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6f09344d1840999c1f6beacbd6d492d7eb4b874
--- /dev/null
+++ b/tests/test_make_data_split.py
@@ -0,0 +1,165 @@
+from unittest import TestCase
+import os
+import h5py
+import numpy as np
+import toml
+import orcasong.tools.make_data_split as mds
+
+__author__ = 'Daniel Guderian'
+
+test_dir = os.path.dirname(os.path.realpath(__file__))
+test_data_dir = os.path.join(test_dir, "data")
+#these are files that were processed with orcasong
+mupage_file = os.path.join(test_data_dir, "processed_data_muon", "processed_graph_muon.h5")
+neutrino_file = os.path.join(test_data_dir,"processed_data_neutrino", "processed_graph_neutrino.h5")
+#config file containing 2 input groups
+config_file = os.path.join(test_data_dir, "test_make_data_split_config.toml")
+#the list files that will be created
+list_file_dir = os.path.join(test_data_dir, "data_split_test_output", "conc_list_files")
+list_output_val = os.path.join(list_file_dir, "test_list_validate_0.txt")
+list_output_train = os.path.join("data_split_test_output", "conc_list_files", "test_list_train_0.txt")
+#the scripts outputs
+scripts_output_dir = os.path.join(test_data_dir, "data_split_test_output", "job_scripts")
+concatenate_bash_script_train = os.path.join(scripts_output_dir, "concatenate_h5_test_list_train_0.sh")
+concatenate_bash_script_val = os.path.join(scripts_output_dir, "concatenate_h5_test_list_validate_0.sh")
+shuffle_bash_script_train = os.path.join(scripts_output_dir, "shuffle_h5_test_list_train_0.sh")
+shuffle_bash_script_val = os.path.join(scripts_output_dir, "shuffle_h5_test_list_validate_0.sh")
+#and the files that will be created from these scripts
+concatenate_file = os.path.join("data_split_test_output", "data_split", "test_list_train_0.h5")
+
+class TestMakeDataSplit(TestCase):
+
+ ''' Runs the make_data_split like in the use case. At the end, the created lists are checked.'''
+
+ @classmethod
+ def setUpClass(cls):
+ #the expected lists to compare to
+ cls.input_categories_list = ["neutrino","muon"]
+ #include name with linebreak as they will look like this in the final files
+ cls.file_path_list = ['processed_data_muon/processed_graph_muon.h5','processed_data_neutrino/processed_graph_neutrino.h5',
+ 'processed_data_muon/processed_graph_muon.h5\n','processed_data_neutrino/processed_graph_neutrino.h5\n']
+ cls.file_path_list_val = ['processed_data_neutrino/processed_graph_neutrino.h5','processed_data_neutrino/processed_graph_neutrino.h5\n']
+ cls.n_events_list = [18,3]
+ cls.contents_concatenate_script = ['concatenate ' + list_output_train + ' --outfile ' + concatenate_file]
+ cls.contents_shuffle_script = ['h5shuffle2 ' + concatenate_file + ' --max_ram 1000000000 \n']
+
+
+ #create list_file_dir
+ if not os.path.exists(list_file_dir):
+ os.makedirs(list_file_dir)
+
+ @classmethod
+ def tearDownClass(cls):
+ #remove the lists created
+ os.remove(list_output_val)
+ os.remove(list_output_train)
+ os.remove(concatenate_bash_script_train)
+ os.remove(concatenate_bash_script_val)
+ os.remove(shuffle_bash_script_train)
+ os.remove(shuffle_bash_script_val)
+ os.removedirs(scripts_output_dir)
+ os.removedirs(list_file_dir)
+ os.removedirs(os.path.join(test_data_dir, "data_split_test_output", "logs"))
+ os.removedirs(os.path.join(test_data_dir, "data_split_test_output", "data_split"))
+
+
+
+ def test_read_keys_off_config(self):
+ self.cfg = read_config(config_file)
+ #get input groups and compare
+ self.ip_group_keys = mds.get_all_ip_group_keys(self.cfg)
+ self.assertSequenceEqual(self.ip_group_keys,self.input_categories_list)
+
+ def test_get_filepath_and_n_events(self):
+ #repeat first 2 steps
+ self.cfg = read_config(config_file)
+ self.ip_group_keys = mds.get_all_ip_group_keys(self.cfg)
+
+ self.cfg,self.n_evts_total = update_cfg(self.cfg)
+
+ for key in self.ip_group_keys:
+ self.assertIn(self.cfg[key]['fpaths'][0],self.file_path_list)
+ self.assertIn(self.cfg[key]['n_evts'],self.n_events_list)
+
+ def test_make_split(self):
+ #main
+ #repeat first 3 steps
+ self.cfg = read_config(config_file)
+ self.ip_group_keys = mds.get_all_ip_group_keys(self.cfg)
+ self.cfg,self.n_evts_total = update_cfg(self.cfg)
+
+ self.cfg['n_evts_total'] = self.n_evts_total
+ mds.print_input_statistics(self.cfg, self.ip_group_keys)
+ for key in self.ip_group_keys:
+ mds.add_fpaths_for_data_split_to_cfg(self.cfg, key)
+ mds.make_dsplit_list_files(self.cfg)
+
+ #assert the single output lists
+ assert os.path.exists(list_output_val) == 1
+ with open(list_output_val) as f:
+ for line in f:
+ self.assertIn(line,self.file_path_list_val)
+ f.close
+
+ assert os.path.exists(list_output_train) == 1
+ with open(list_output_train) as f2:
+ for line in f2:
+ self.assertIn(line,self.file_path_list)
+ f2.close
+
+ def test_make_concatenate_and_shuffle_scripts(self):
+ #main
+ #repeat first 4 steps
+ self.cfg = read_config(config_file)
+ self.ip_group_keys = mds.get_all_ip_group_keys(self.cfg)
+ self.cfg,self.n_evts_total = update_cfg(self.cfg)
+
+ self.cfg['n_evts_total'] = self.n_evts_total
+ mds.print_input_statistics(self.cfg, self.ip_group_keys)
+ for key in self.ip_group_keys:
+ mds.add_fpaths_for_data_split_to_cfg(self.cfg, key)
+ mds.make_dsplit_list_files(self.cfg)
+
+ #create the bash job scripts and test their content
+ mds.make_concatenate_and_shuffle_scripts(self.cfg)
+
+ assert os.path.exists(concatenate_bash_script_train) == 1
+ with open(concatenate_bash_script_train) as f:
+ for line in f:
+ pass #yay, awesome style! ^^
+ last_line = line
+ self.assertIn(last_line,self.contents_concatenate_script)
+ f.close
+
+ assert os.path.exists(shuffle_bash_script_train) == 1
+ with open(shuffle_bash_script_train) as f2:
+ for line in f2:
+ pass
+ last_line = line
+ self.assertIn(last_line,self.contents_shuffle_script)
+ f2.close
+
+
+def update_cfg(cfg):
+
+ ''' Update the cfg with file paths and also return the total number of events'''
+
+ #get input groups and compare
+ ip_group_keys = mds.get_all_ip_group_keys(cfg)
+ os.chdir(test_data_dir)
+ n_evts_total = 0
+ for key in ip_group_keys:
+ print('Collecting information from input group ' + key)
+ cfg[key]['fpaths'] = mds.get_h5_filepaths(cfg[key]['dir'])
+ cfg[key]['n_files'] = len(cfg[key]['fpaths'])
+ cfg[key]['n_evts'], cfg[key]['n_evts_per_file_mean'], cfg[key]['run_ids'] = mds.get_number_of_evts_and_run_ids(cfg[key]['fpaths'], dataset_key='y')
+ n_evts_total += cfg[key]['n_evts']
+
+ return cfg,n_evts_total
+
+def read_config(config_file):
+ #decode config
+ cfg = toml.load(config_file)
+ cfg['toml_filename'] = config_file
+ return cfg
+