class CSVConverterSolarAnywhere(CSVConverter):
def __init__(self, logger):
self.logger = logger
self.parser = ParserSolarAnywhere(self.logger)
self.pandas_tools = PandasTools(self.parser, self.logger)
def input_csv_to_df(self, input_file, date_range, plot_output_path):
csv_converter = CSVConverter(self.parser, self.logger)
# "0_0.csv" => "0_0"
column_str = input_file.filename.split(".")[0]
columns = [
column_str + '_' + column for column in ['GHI', 'DNI', 'DHI']
]
csv_converter.input_csv_to_df(input_file, date_range, columns)
df = csv_converter.pandas_tools.df.copy()
ParserSolarAnywhere.plot(plot_output_path, input_file.filename, df)
self.pandas_tools.concat_df(csv_converter.pandas_tools.df)
def df_to_output_csv(self, output_path, output_filename):
output_file = CSVWriter(output_path, output_filename)
output_file.write_row(['DATASET:', self.parser.NAME])
columns = ['Timestamp']
columns.extend(self.pandas_tools.df.columns)
output_file.write_row(columns)
self.pandas_tools.df_to_csv(output_file)
output_file.close()
class CSVConverterADCP(CSVConverter):
def __init__(self, logger):
self.logger = logger
self.parser = ParserADCP(self.logger)
self.pandas_tools = PandasTools(self.parser, self.logger)
def input_csv_to_df(self, input_file, date_range):
csv_converter = CSVConverter(self.parser, self.logger)
# "TAO_T0N110W_D_ADCP.ascii" => "T0N110W"
column_str = input_file.filename.split("_")[1]
columns = self.columns(column_str)
csv_converter.input_csv_to_df(input_file, date_range, columns,
column_str)
self.pandas_tools.concat_df(csv_converter.pandas_tools.df)
@classmethod
def columns(cls, column_str):
cols = []
for depth in range(10, 321, 5): # [10, 15, 20, ..., 310, 315, 320]
for component in ['UCUR', 'VCUR', 'WCUR']:
col = column_str + "_" + str(int(depth)) + "_" + component
cols.append(col)
return cols
def df_to_output_csv(self, output_path, output_filename):
output_file = CSVWriter(output_path, output_filename)
output_file.write_row(['DATASET:', self.parser.NAME])
columns = ['Timestamp']
columns.extend(self.pandas_tools.df.columns)
output_file.write_row(columns)
self.pandas_tools.df_to_csv(output_file)
output_file.close()
class DataFrameUtils:
def __init__(self, parser, logger):
self.parser = parser
self.logger = logger
self.pandas_tools = PandasTools(self.parser, self.logger)
self.current_pandas_tools = None
def buoy_start(self, new_buoy_id):
print "new buoy", new_buoy_id
columns = [
"lat", "long", "ZonWinds", "MerWinds", "Humidity", "AirTemp", "SST"
]
columns = ["%s_%s" % (new_buoy_id, column) for column in columns]
if new_buoy_id != 1: # this is only False the first time this method is called
self.buoy_end()
self.current_pandas_tools = PandasTools(self.parser, self.logger)
self.current_pandas_tools.new_df(columns, False)
def add_row(self, timestamp, row):
self.current_pandas_tools.add_row(timestamp, row)
def buoy_end(self):
self.pandas_tools.concat_df(self.current_pandas_tools.df)
def df_to_output_csv(self, output_path, output_filename):
# print stats
self.pandas_tools.print_stats()
# output to file
output_file = CSVWriter(output_path, output_filename)
output_file.write_row(['DATASET:', self.parser.NAME])
output_file.write_row(['TIME UNIT:', 'hours'])
output_file.write_row(
['FIRST TIMESTAMP:',
self.pandas_tools.first_timestamp()])
row = ['Time Delta']
row.extend(self.pandas_tools.df.columns)
output_file.write_row(row)
self.pandas_tools.df_to_csv(output_file, 'hours')
output_file.close()
class CSVConverterNOAA(CSVConverter):
def __init__(self, logger):
self.logger = logger
self.parser = ParserNOAA(self.logger)
self.pandas_tools = PandasTools(self.parser, self.logger)
def input_csv_to_df(self, input_file, date_range):
csv_converter = CSVConverter(self.parser, self.logger)
# "TAO_T5N140W_D_SST_10min.ascii" => "T5N140W"
column = input_file.filename.split("_")[1]
csv_converter.input_csv_to_df(input_file, date_range, [column])
self.pandas_tools.concat_df(csv_converter.pandas_tools.df)
def df_to_output_csv(self, output_path, output_filename):
output_file = CSVWriter(output_path, output_filename)
output_file.write_row(['DATASET:', self.parser.NAME])
columns = ['Timestamp']
columns.extend(self.pandas_tools.df.columns)
output_file.write_row(columns)
self.pandas_tools.df_to_csv(output_file)
output_file.close()