def test_3():
objective = "read_time()"
reason = "The function is not converting the time correctly, check the math"
adict = {1: [1593633600, 1593637200, 1593640800, 1593644400, 1593648000]}
result = RC.read_time(adict)
expected = [16, 17, 18, 19, 20]
assert result[1] == expected, str_form(objective, result, expected, reason)
def test_1():
objective = "read_time()"
reason = "The function is not converting the time correctly, check the math"
adict = {1: [1593633600]}
result = RC.read_time(adict)
time = DT.datetime.utcfromtimestamp(1593633600)
expected = [int(f"{time:%H}") - 4]
assert result[1] == expected, str_form(objective, result, expected, reason)
def test_10():
objective = "time_and_temp()"
reason = "The function is not working correctly"
x = RC.read_weather()
time_dict = RC.read_time(x[2])
temp_dict = x[0]
expected = time_and_temp(temp_dict, time_dict)
result = time_and_temp(temp_dict, time_dict)
print(result)
assert result == expected, str_form(objective, result, expected, reason)
def test_4():
objective = "read_time()"
reason = "The function is not converting the time correctly, check the math"
adict = {
1: [
1593633600, 1593637200, 1593640800, 1593644400, 1593648000,
1593651600, 1593655200, 1593658800, 1593662400, 1593666000,
1593669600, 1593673200, 1593676800, 1593680400
]
}
result = RC.read_time(adict)
expected = [16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5]
assert result[1] == expected, str_form(objective, result, expected, reason)
def data_frame(period, data, weathertype):
"""
The purpose is to put the data into a single panda data frame
:param: period: The period of time that we will take avg,min,max data
:param: data: The weather data from the readcsv file
:param: weathertype is what data you want in a data frame. Can be Temp, Pressure, Humidity
:return: The data frame with all the data organized and the list of the indexes
"""
data_type = {'Temperature': 0, 'Pressure': 3, 'Humidity': 4}
all_data = data # All of the data that is read from the read_weather function in readcsv file
all_time_data = RC.read_time(
all_data[2]) # Gets the time data and puts it in 24 hour format
sorted_dict = sorted_data(all_data[data_type[weathertype]], all_time_data)
volatility(sorted_dict, period)
# The dictionary that will be used to make the data frame
dataframe_dict = {
'Time': [],
'Actual': [],
'Avg': [],
'Min': [],
'Max': []
}
axis_index = list() # Will become the lines in the left column
for line in all_time_data: # Loops through every line from the file
axis_index.append(
line) # Puts the number of lines in a list. in ascending order
time = all_time_data[line][
0] # Finds the first time values from each line
volatility_data = sorted_dict[time]
dataframe_dict['Time'].append(
time) # Adds the first time in each line into a list
dataframe_dict['Actual'].append(volatility_data['Act'].pop(0))
dataframe_dict['Avg'].append(volatility_data['Avg'].pop(0))
dataframe_dict['Min'].append(volatility_data['Min'].pop(0))
dataframe_dict['Max'].append(volatility_data['Max'].pop(0))
dataframe = pd.DataFrame(dataframe_dict, axis_index)
return dataframe, axis_index