<commit_before># Copyright (c) 2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
=========================================
Surface Analysis using Declarative Syntax
=========================================

The MetPy declarative syntax allows for a simplified interface to creating common
meteorological analyses including surface observation plots.
"""

########################################
from datetime import datetime

import pandas as pd

from metpy.cbook import get_test_data
import metpy.plots as mpplots
from metpy.units import units


########################################
# **Getting the data**
#
# In this example, data is originally from the Iowa State Upper-air archive
# (https://mesonet.agron.iastate.edu/archive/raob/) available through a Siphon method.
# The data are pre-processed to attach latitude/lognitude locations for each RAOB site.

data = pd.read_csv(get_test_data('UPA_obs.csv', as_file_obj=False))

########################################
# **Plotting the data**
#<commit_msg>BUG: Fix copy paste in upper air declarative example<commit_after># Copyright (c) 2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
===========================================
Upper Air Analysis using Declarative Syntax
===========================================

The MetPy declarative syntax allows for a simplified interface to creating common
meteorological analyses including upper air observation plots.
"""

########################################
from datetime import datetime

import pandas as pd

from metpy.cbook import get_test_data
import metpy.plots as mpplots
from metpy.units import units


########################################
# **Getting the data**
#
# In this example, data is originally from the Iowa State Upper-air archive
# (https://mesonet.agron.iastate.edu/archive/raob/) available through a Siphon method.
# The data are pre-processed to attach latitude/longitude locations for each RAOB site.

data = pd.read_csv(get_test_data('UPA_obs.csv', as_file_obj=False))

########################################
# **Plotting the data**
#
{'fraction_non_alphanumeric': 0.24562191288729232, 'fraction_numerical': 0.005388414907947912, 'mean_word_length': 6.210355987055016, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4539348', 'n_tokens_mistral': 596, 'n_tokens_neox': 547, 'n_words': 208}