def chil_info(data, units):
""""Gets the wind chill info."""
# Get the data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
chil_median = calculations.median(chil_data)
chil_range = calculations.range(chil_data)
chil_mode, chil_mode_count = calculations.mode(chil_data)
# Create the data list.
data2 = [["Lowest wind chill",
"%.2f %s" % (chil_low, units["temp"])],
["Highest wind chill",
"%.2f %s" % (chil_high, units["temp"])],
["Average wind chill",
"%.2f %s" % (chil_avg, units["temp"])],
["Median wind chill",
"%.2f %s" % (chil_median, units["temp"])],
["Range of wind chills",
"%.2f %s" % (chil_range, units["temp"])],
[
"Most common wind chill",
"%.2f %s (%d occurrences)" %
(chil_mode, units["temp"], chil_mode_count)
]]
return data2
def check_used(self, field):
"""Checks if the selected field has already been used."""
if field in datasets.get_column(self.conditions, 0):
show_error_dialog(self, "Add Condition", "A condition for %s has already been entered." % field)
return True
return False
def temp_info(data, units):
""""Gets the temperature info."""
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
temp_median = calculations.median(temp_data)
temp_range = calculations.range(temp_data)
temp_mode, temp_mode_count = calculations.mode(temp_data)
# Create the data list.
data2 = [["Lowest temperature",
"%.2f %s" % (temp_low, units["temp"])],
["Highest temperature",
"%.2f %s" % (temp_high, units["temp"])],
["Average temperature",
"%.2f %s" % (temp_avg, units["temp"])],
["Median temperature",
"%.2f %s" % (temp_median, units["temp"])],
[
"Range of temperatures",
"%.2f %s" % (temp_range, units["temp"])
],
[
"Most common temperature",
"%.2f %s (%d occurrences)" %
(temp_mode, units["temp"], temp_mode_count)
]]
return data2
def airp_info(data, units):
"""Gets the air pressure info."""
# Get the data.
num_days = len(data)
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
airp_median = calculations.median(airp_data1)
airp_range = calculations.range(airp_data1)
airp_mode, airp_mode_count = calculations.mode(airp_data1)
airp_steady = 0
airp_rising = 0
airp_falling = 0
for i in airp_data2:
if i == "Steady":
airp_steady += 1
elif i == "Rising":
airp_rising += 1
elif i == "Falling":
airp_falling += 1
# Create the data list.
data2 = [
["Lowest air pressure",
"%.2f %s" % (airp_low, units["airp"])],
["Highest air pressure",
"%.2f %s" % (airp_high, units["airp"])],
["Average air pressure",
"%.2f %s" % (airp_avg, units["airp"])],
["Median air pressure",
"%.2f %s" % (airp_median, units["airp"])],
["Range of air pressures",
"%.2f %s" % (airp_range, units["airp"])],
[
"Most common air pressure",
"%.2f %s (%d occurrences)" %
(airp_mode, units["airp"], airp_mode_count)
],
[
"Days with steady pressure",
"%d day%s (%d%%)" % (airp_steady, "" if airp_steady == 1 else "s",
(airp_steady / num_days) * 100)
],
[
"Days with rising pressure",
"%d day%s (%d%%)" % (airp_rising, "" if airp_rising == 1 else "s",
(airp_rising / num_days) * 100)
],
[
"Days with falling pressure",
"%d day%s (%d%%)" %
(airp_falling, "" if airp_falling == 1 else "s",
(airp_falling / num_days) * 100)
]
]
return data2
def visi_info(data, units):
""""Gets the visibility info."""
# Get the data.
visi_data = datasets.convert_float(datasets.get_column(data, 2))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
visi_median = calculations.median(visi_data)
visi_range = calculations.range(visi_data)
visi_mode, visi_mode_count = calculations.mode(visi_data)
# Create the data list.
data2 = [["Lowest visibility",
"%.2f %s" % (visi_low, units["visi"])],
["Highest visibility",
"%.2f %s" % (visi_high, units["visi"])],
["Average visibility",
"%.2f %s" % (visi_avg, units["visi"])],
["Median visibility",
"%.2f %s" % (visi_median, units["visi"])],
["Range of visibility",
"%.2f %s" % (visi_range, units["visi"])],
[
"Most common visibility",
"%.2f %s (%d occurrences)" %
(visi_mode, units["visi"], visi_mode_count)
]]
return data2
def humi_chart(data, units):
""""Gets the humidity chart data."""
# Get the data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
humi_median = calculations.median(humi_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
humi = [data[i][0], "%.2f%%" % (humi_data[i])]
humi += build_chart(humi_data[i],
humi_low,
humi_high,
humi_avg,
humi_median,
"%",
unit_space=False)
data2.append(humi)
return data2
def prec_chart(data, units):
""""Gets the precipitation chart data."""
# Get the data.
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_split = datasets.split_list2(datasets.get_column(data, 3))
prec_data1 = datasets.none_to_zero(prec_data1)
prec_data1 = datasets.convert_float(prec_data1)
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
prec_median = calculations.median(prec_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
prec = [data[i][0], "%.2f %s" % (prec_data1[i], units["prec"])]
if prec_avg == prec_data1[i]:
average = "Average Value"
else:
average = prec_avg - prec_data1[i]
average = "%.2f %s %s" % (abs(average), units["prec"], "above" if prec_avg < prec_data1[i] else "below")
prec.append(average)
if prec_low == prec_data1[i]:
low = "Lowest Value"
else:
low = prec_low - prec_data1[i]
low = "%.2f %s %s" % (abs(low), units["prec"], "above" if prec_low < prec_data1[i] else "below")
prec.append(low)
if prec_high == prec_data1[i]:
high = "Highest Value"
else:
high = prec_high - prec_data1[i]
high = "%.2f %s %s" % (abs(high), units["prec"], "above" if prec_high < prec_data1[i] else "below")
prec.append(high)
if prec_median == prec_data1[i]:
median = "Median Value"
else:
median = prec_median - prec_data1[i]
median = "%.2f %s %s" % (abs(median), units["prec"], "above" if prec_median < prec_data1[i] else "below")
prec.append(median)
data2.append(prec)
return data2
def get_restore_data(main_dir,
conf_dir,
config,
default_width,
default_height,
default_dataset="Main Dataset"):
"""Gets the last window size."""
try:
rest_file = open("%s/application_restore.json" % conf_dir, "r")
rest_data = json.load(rest_file)
rest_file.close()
last_width = rest_data["window_width"]
last_height = rest_data["window_height"]
last_dataset = rest_data["last_dataset"]
except IOError as e:
print(
"get_window_size(): Error reading restore file (IOError):\n%s" % e,
"Continuing with default...")
last_width = default_width
last_height = default_height
last_dataset = default_dataset
except (TypeError, ValueError) as e:
print(
"get_window_size(): Error reading restore file (TypeError or ValueError):\n%s"
% e, "Continuing with default...")
last_width = default_width
last_height = default_height
last_dataset = default_dataset
# Check if the dataset exists.
dataset_list = datasets.get_column(
io.get_dataset_list(main_dir, "", False), 0)
if last_dataset in dataset_list:
dataset_exists = True
original_dataset = ""
else:
dataset_exists = False
original_dataset = last_dataset
# If the dataset doesn't exist, switch or make one that does:
if not dataset_exists:
# If the default dataset exists, switch to that.
if "Main Dataset" in dataset_list:
last_dataset = "Main Dataset"
# Otherwise, create the default dataset.
else:
ensure_files_exist(main_dir, conf_dir)
# If the user doesn't want to restore the window size, set the size to the defaults.
if not config["restore"]:
return last_dataset, original_dataset, dataset_exists, default_width, default_height
return last_dataset, original_dataset, dataset_exists, last_width, last_height
def check_used(self, field):
"""Checks if the selected field has already been used."""
if field in datasets.get_column(self.conditions, 0):
show_error_dialog(
self, "Add Condition",
"A condition for %s has already been entered." % field)
return True
return False
def filter_not(set1, data):
"""Returns a list of the items in data that are not in set1."""
# Get the date column of the set for the comparison.
filtered = []
date_list = datasets.get_column(set1, 0)
# Loop through the data list, and only add the item to the filtered list if it's not in the other set.
for i in data:
if i[0] not in date_list:
filtered.append(i)
return filtered
def filter_and(set1, set2):
"""Returns a list of the items that are in both set1 and set2."""
# Get the date column of the second list for the comparison.
filtered = []
date_list = datasets.get_column(set2, 0)
# Loop through the first set, and only add the item to the filtered list if it's also in the second set.
for i in set1:
if i[0] in date_list:
filtered.append(i)
return filtered
def filter_and(set1, set2):
"""Returns a list of the items that are in both set1 and set2."""
# Get the date column of the second list for the comparison.
filtered = []
date_list = datasets.get_column(set2, 0)
# Loop through the first set, and only add the item to the filtered list if it's also in the second set.
for i in set1:
if i[0] in date_list:
filtered.append(i)
return filtered
def filter_not(set1, data):
"""Returns a list of the items in data that are not in set1."""
# Get the date column of the set for the comparison.
filtered = []
date_list = datasets.get_column(set1, 0)
# Loop through the data list, and only add the item to the filtered list if it's not in the other set.
for i in data:
if i[0] not in date_list:
filtered.append(i)
return filtered
def filter_or(set1, set2):
"""Returns a list of the items that are in either set1 or set2."""
# Get the date column of the first set for the comparison.
date_list = datasets.get_column(set1, 0)
# Loop through the second list, and add the item if it isn't already in the first list.
filtered = set1[:]
for i in set2:
if i[0] not in date_list:
filtered.append(i)
filtered = sorted(
filtered, key=lambda x: datetime.datetime.strptime(x[0], "%d/%m/%Y"))
return filtered
def wind_info(data, units):
"""Gets the wind info."""
# Get the data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.none_to_zero(wind_data1)
wind_data1 = datasets.convert_float(wind_data1)
try:
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
wind_median = calculations.median(wind_data1)
wind_range = calculations.range(wind_data1)
except ZeroDivisionError:
wind_low = "None"
wind_high = "None"
wind_avg = "None"
wind_median = "None"
wind_range = "None"
wind_mode, wind_mode_count = calculations.mode(wind_data2)
# Change any values, if needed.
wind_low = "None" if wind_low == "None" else ("%.2f %s" %
(wind_low, units["wind"]))
wind_high = "None" if wind_high == "None" else ("%.2f %s" %
(wind_high, units["wind"]))
wind_avg = "None" if wind_avg == "None" else ("%.2f %s" %
(wind_avg, units["wind"]))
wind_median = "None" if wind_median == "None" else (
"%.2f %s" % (wind_median, units["wind"]))
wind_range = "None" if wind_range == "None" else (
"%.2f %s" % (wind_range, units["wind"]))
# Create the data list.
data2 = [["Lowest wind speed",
wind_low], ["Highest wind speed", wind_high],
["Average wind speed", wind_avg],
["Median wind speed", wind_median],
["Range of wind speeds", wind_range],
[
"Most common wind direction",
"%s (%d occurrences)" %
(wind_mode if wind_mode != "" else "None", wind_mode_count)
]]
return data2
def wind_chart(data, units):
""""Gets the wind chart data."""
# Get the data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.none_to_zero(wind_data1)
wind_data1 = datasets.convert_float(wind_data1)
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
wind_median = calculations.median(wind_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
wind = [data[i][0], "%.2f %s" % (wind_data1[i], units["wind"])]
if wind_avg == wind_data1[i]:
average = "Average Value"
else:
average = wind_avg - wind_data1[i]
average = "%.2f %s %s" % (abs(average), units["wind"], "above" if wind_avg < wind_data1[i] else "below")
wind.append(average)
if wind_low == wind_data1[i]:
low = "Lowest Value"
else:
low = wind_low - wind_data1[i]
low = "%.2f %s %s" % (abs(low), units["wind"], "above" if wind_low < wind_data1[i] else "below")
wind.append(low)
if wind_high == wind_data1[i]:
high = "Highest Value"
else:
high = wind_high - wind_data1[i]
high = "%.2f %s %s" % (abs(high), units["wind"], "above" if wind_high < wind_data1[i] else "below")
wind.append(high)
if wind_median == wind_data1[i]:
median = "Median Value"
else:
median = wind_median - wind_data1[i]
median = "%.2f %s %s" % (abs(median), units["wind"], "above" if wind_median < wind_data1[i] else "below")
wind.append(median)
data2.append(wind)
return data2
def filter_or(set1, set2):
"""Returns a list of the items that are in either set1 or set2."""
# Get the date column of the first set for the comparison.
date_list = datasets.get_column(set1, 0)
# Set the filtered list to all of the items of the first set.
filtered = set1[:]
# Loop through the second list, and add the item if it isn't already in the first list.
for i in set2:
if i[0] not in date_list:
filtered.append(i)
# Sort the filtered list.
filtered = sorted(filtered, key = lambda x: datetime.datetime.strptime(x[0], "%d/%m/%Y"))
return filtered
def airp_chart(data, units):
""""Gets the air pressure chart data."""
# Get the data.
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
airp_median = calculations.median(airp_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
airp = [data[i][0], "%.2f %s" % (airp_data1[i], units["airp"])]
if airp_avg == airp_data1[i]:
average = "Average Value"
else:
average = airp_avg - airp_data1[i]
average = "%.2f %s %s" % (abs(average), units["airp"], "above" if airp_avg < airp_data1[i] else "below")
airp.append(average)
if airp_low == airp_data1[i]:
low = "Lowest Value"
else:
low = airp_low - airp_data1[i]
low = "%.2f %s %s" % (abs(low), units["airp"], "above" if airp_low < airp_data1[i] else "below")
airp.append(low)
if airp_high == airp_data1[i]:
high = "Highest Value"
else:
high = airp_high - airp_data1[i]
high = "%.2f %s %s" % (abs(high), units["airp"], "above" if airp_high < airp_data1[i] else "below")
airp.append(high)
if airp_median == airp_data1[i]:
median = "Median Value"
else:
median = airp_median - airp_data1[i]
median = "%.2f %s %s" % (abs(median), units["airp"], "above" if airp_median < airp_data1[i] else "below")
airp.append(median)
data2.append(airp)
return data2
def humi_chart(data, units):
""""Gets the humidity chart data."""
# Get the data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
humi_median = calculations.median(humi_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
humi = [data[i][0], "%.2f%%" % (humi_data[i])]
if humi_avg == humi_data[i]:
average = "Average Value"
else:
average = humi_avg - humi_data[i]
average = "%.2f%% %s" % (abs(average), "above" if humi_avg < humi_data[i] else "below")
humi.append(average)
if humi_low == humi_data[i]:
low = "Lowest Value"
else:
low = humi_low - humi_data[i]
low = "%.2f%% %s" % (abs(low), "above" if humi_low < humi_data[i] else "below")
humi.append(low)
if humi_high == humi_data[i]:
high = "Highest Value"
else:
high = humi_high - humi_data[i]
high = "%.2f%% %s" % (abs(high), "above" if humi_high < humi_data[i] else "below")
humi.append(high)
if humi_median == humi_data[i]:
median = "Median Value"
else:
median = humi_median - humi_data[i]
median = "%.2f%% %s" % (abs(median), "above" if humi_median < humi_data[i] else "below")
humi.append(median)
data2.append(humi)
return data2
def temp_chart(data, units):
""""Gets the temperature chart data."""
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
temp_median = calculations.median(temp_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
temp = [data[i][0], "%.2f %s" % (temp_data[i], units["temp"])]
if temp_avg == temp_data[i]:
average = "Average Value"
else:
average = temp_avg - temp_data[i]
average = "%.2f %s %s" % (abs(average), units["temp"], "above" if temp_avg < temp_data[i] else "below")
temp.append(average)
if temp_low == temp_data[i]:
low = "Lowest Value"
else:
low = temp_low - temp_data[i]
low = "%.2f %s %s" % (abs(low), units["temp"], "above" if temp_low < temp_data[i] else "below")
temp.append(low)
if temp_high == temp_data[i]:
high = "Highest Value"
else:
high = temp_high - temp_data[i]
high = "%.2f %s %s" % (abs(high), units["temp"], "above" if temp_high < temp_data[i] else "below")
temp.append(high)
if temp_median == temp_data[i]:
median = "Median Value"
else:
median = temp_median - temp_data[i]
median = "%.2f %s %s" % (abs(median), units["temp"], "above" if temp_median < temp_data[i] else "below")
temp.append(median)
data2.append(temp)
return data2
def chil_chart(data, units):
""""Gets the wind chill chart data."""
# Get the data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
chil_median = calculations.median(chil_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
chil = [data[i][0], "%.2f %s" % (chil_data[i], units["temp"])]
if chil_avg == chil_data[i]:
average = "Average Value"
else:
average = chil_avg - chil_data[i]
average = "%.2f %s %s" % (abs(average), units["temp"], "above" if chil_avg < chil_data[i] else "below")
chil.append(average)
if chil_low == chil_data[i]:
low = "Lowest Value"
else:
low = chil_low - chil_data[i]
low = "%.2f %s %s" % (abs(low), units["temp"], "above" if chil_low < chil_data[i] else "below")
chil.append(low)
if chil_high == chil_data[i]:
high = "Highest Value"
else:
high = chil_high - chil_data[i]
high = "%.2f %s %s" % (abs(high), units["temp"], "above" if chil_high < chil_data[i] else "below")
chil.append(high)
if chil_median == chil_data[i]:
median = "Median Value"
else:
median = chil_median - chil_data[i]
median = "%.2f %s %s" % (abs(median), units["temp"], "above" if chil_median < chil_data[i] else "below")
chil.append(median)
data2.append(chil)
return data2
def visi_chart(data, units):
""""Gets the visibility chart data."""
# Get the data.
visi_data = datasets.convert_float(datasets.get_column(data, 7))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
visi_median = calculations.median(visi_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
visi = [data[i][0], "%.2f %s" % (visi_data[i], units["visi"])]
if visi_avg == visi_data[i]:
average = "Average Value"
else:
average = visi_avg - visi_data[i]
average = "%.2f %s %s" % (abs(average), units["visi"], "above" if visi_avg < visi_data[i] else "below")
visi.append(average)
if visi_low == visi_data[i]:
low = "Lowest Value"
else:
low = visi_low - visi_data[i]
low = "%.2f %s %s" % (abs(low), units["visi"], "above" if visi_low < visi_data[i] else "below")
visi.append(low)
if visi_high == visi_data[i]:
high = "Highest Value"
else:
high = visi_high - visi_data[i]
high = "%.2f %s %s" % (abs(high), units["visi"], "above" if visi_high < visi_data[i] else "below")
visi.append(high)
if visi_median == visi_data[i]:
median = "Median Value"
else:
median = visi_median - visi_data[i]
median = "%.2f %s %s" % (abs(median), units["visi"], "above" if visi_median < visi_data[i] else "below")
visi.append(median)
data2.append(visi)
return data2
def temp_chart(data, units):
""""Gets the temperature chart data."""
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
temp_median = calculations.median(temp_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
temp = [data[i][0], "%.2f %s" % (temp_data[i], units["temp"])]
temp += build_chart(temp_data[i], temp_low, temp_high, temp_avg,
temp_median, units["temp"])
data2.append(temp)
return data2
def visi_chart(data, units):
""""Gets the visibility chart data."""
# Get the data.
visi_data = datasets.convert_float(datasets.get_column(data, 7))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
visi_median = calculations.median(visi_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
visi = [data[i][0], "%.2f %s" % (visi_data[i], units["visi"])]
visi += build_chart(visi_data[i], visi_low, visi_high, visi_avg,
visi_median, units["visi"])
data2.append(visi)
return data2
def chil_chart(data, units):
""""Gets the wind chill chart data."""
# Get the data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
chil_median = calculations.median(chil_data)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
chil = [data[i][0], "%.2f %s" % (chil_data[i], units["temp"])]
chil += build_chart(chil_data[i], chil_low, chil_high, chil_avg,
chil_median, units["temp"])
data2.append(chil)
return data2
def remove_condition(self, widget):
"""Removes the selected condition."""
# Get the selected conditions.
model, treeiter = self.treeview.get_selection().get_selected_rows()
conds = []
for i in treeiter:
conds.append(model[i][0])
# Don't continue if nothing was selected.
if len(conds) == 0:
return
# Remove the conditions and update the UI.
for i in conds:
index = datasets.get_column(self.conditions, 0).index(i)
del self.conditions[index]
self.liststore.clear()
for i in self.conditions:
self.liststore.append(i)
def remove_condition(self, widget):
"""Removes the selected condition."""
# Get the selected conditions.
model, treeiter = self.treeview.get_selection().get_selected_rows()
conds = []
for i in treeiter:
conds.append(model[i][0])
# Don't continue if nothing was selected.
if len(conds) == 0:
return
# Remove the conditions and update the UI.
for i in conds:
index = datasets.get_column(self.conditions, 0).index(i)
del self.conditions[index]
self.liststore.clear()
for i in self.conditions:
self.liststore.append(i)
def airp_chart(data, units):
""""Gets the air pressure chart data."""
# Get the data.
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
airp_median = calculations.median(airp_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
airp = [data[i][0], "%.2f %s" % (airp_data1[i], units["airp"])]
airp += build_chart(airp_data1[i], airp_low, airp_high, airp_avg,
airp_median, units["airp"])
data2.append(airp)
return data2
def wind_info(data, units):
"""Gets the wind info."""
# Get the data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.none_to_zero(wind_data1)
wind_data1 = datasets.convert_float(wind_data1)
try:
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
wind_median = calculations.median(wind_data1)
wind_range = calculations.range(wind_data1)
except:
wind_low = "None"
wind_high = "None"
wind_avg = "None"
wind_median = "None"
wind_range = "None"
wind_mode, wind_mode_count = calculations.mode(wind_data2)
# Change any values, if needed.
wind_low = "None" if wind_low == "None" else ("%.2f %s" % (wind_low, units["wind"]))
wind_high = "None" if wind_high == "None" else ("%.2f %s" % (wind_high, units["wind"]))
wind_avg = "None" if wind_avg == "None" else ("%.2f %s" % (wind_avg, units["wind"]))
wind_median = "None" if wind_median == "None" else ("%.2f %s" % (wind_median, units["wind"]))
wind_range = "None" if wind_range == "None" else ("%.2f %s" % (wind_range, units["wind"]))
# Create the data list.
data2 = [
["Lowest wind speed", wind_low],
["Highest wind speed", wind_high],
["Average wind speed", wind_avg],
["Median wind speed", wind_median],
["Range of wind speeds", wind_range],
["Most common wind direction", "%s (%d occurrences)" % (wind_mode if wind_mode != "" else "None", wind_mode_count)]
]
return data2
def prec_chart(data, units):
""""Gets the precipitation chart data."""
# Get the data.
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_data1 = datasets.none_to_zero(prec_data1)
prec_data1 = datasets.convert_float(prec_data1)
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
prec_median = calculations.median(prec_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
prec = [data[i][0], "%.2f %s" % (prec_data1[i], units["prec"])]
prec += build_chart(prec_data1[i], prec_low, prec_high, prec_avg,
prec_median, units["prec"])
data2.append(prec)
return data2
def wind_chart(data, units):
""""Gets the wind chart data."""
# Get the data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.none_to_zero(wind_data1)
wind_data1 = datasets.convert_float(wind_data1)
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
wind_median = calculations.median(wind_data1)
# Calculate and add the data.
data2 = []
for i in range(0, len(data)):
wind = [data[i][0], "%.2f %s" % (wind_data1[i], units["wind"])]
wind += build_chart(wind_data1[i], wind_low, wind_high, wind_avg,
wind_median, units["wind"])
data2.append(wind)
return data2
def airp_info(data, units):
"""Gets the air pressure info."""
# Get the data.
num_days = len(data)
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
airp_median = calculations.median(airp_data1)
airp_range = calculations.range(airp_data1)
airp_mode, airp_mode_count = calculations.mode(airp_data1)
airp_steady = 0
airp_rising = 0
airp_falling = 0
for i in airp_data2:
if i == "Steady":
airp_steady += 1
elif i == "Rising":
airp_rising += 1
elif i == "Falling":
airp_falling += 1
# Create the data list.
data2 = [
["Lowest air pressure", "%.2f %s" % (airp_low, units["airp"])],
["Highest air pressure", "%.2f %s" % (airp_high, units["airp"])],
["Average air pressure", "%.2f %s" % (airp_avg, units["airp"])],
["Median air pressure", "%.2f %s" % (airp_median, units["airp"])],
["Range of air pressures", "%.2f %s" % (airp_range, units["airp"])],
["Most common air pressure", "%.2f %s (%d occurrences)" % (airp_mode, units["airp"], airp_mode_count)],
["Days with steady pressure", "%d day%s (%d%%)" % (airp_steady, "" if airp_steady == 1 else "s", (airp_steady / num_days) * 100)],
["Days with rising pressure", "%d day%s (%d%%)" % (airp_rising, "" if airp_rising == 1 else "s", (airp_rising / num_days) * 100)],
["Days with falling pressure", "%d day%s (%d%%)" % (airp_falling, "" if airp_falling == 1 else "s", (airp_falling / num_days) * 100)]
]
return data2
def visi_info(data, units):
""""Gets the visibility info."""
# Get the data.
visi_data = datasets.convert_float(datasets.get_column(data, 2))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
visi_median = calculations.median(visi_data)
visi_range = calculations.range(visi_data)
visi_mode, visi_mode_count = calculations.mode(visi_data)
# Create the data list.
data2 = [
["Lowest visibility", "%.2f %s" % (visi_low, units["visi"])],
["Highest visibility", "%.2f %s" % (visi_high, units["visi"])],
["Average visibility", "%.2f %s" % (visi_avg, units["visi"])],
["Median visibility", "%.2f %s" % (visi_median, units["visi"])],
["Range of visibility", "%.2f %s" % (visi_range, units["visi"])],
["Most common visibility", "%.2f %s (%d occurrences)" % (visi_mode, units["visi"], visi_mode_count)]
]
return data2
def humi_info(data, units):
"""Gets the humidity info."""
# Get the data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
humi_median = calculations.median(humi_data)
humi_range = calculations.range(humi_data)
humi_mode, humi_mode_count = calculations.mode(humi_data)
# Create the data list.
data2 = [
["Lowest humidity", "%.2f%%" % humi_low],
["Highest humidity", "%.2f%%" % humi_high],
["Average humidity", "%.2f%%" % humi_avg],
["Median humidity", "%.2f%%" % humi_median],
["Range of humidity", "%.2f%%" % humi_range],
["Most common humidity", "%.2f%% (%d occurrences)" % (humi_mode, humi_mode_count)]
]
return data2
def chil_info(data, units):
""""Gets the wind chill info."""
# Get the data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
chil_median = calculations.median(chil_data)
chil_range = calculations.range(chil_data)
chil_mode, chil_mode_count = calculations.mode(chil_data)
# Create the data list.
data2 = [
["Lowest wind chill", "%.2f %s" % (chil_low, units["temp"])],
["Highest wind chill", "%.2f %s" % (chil_high, units["temp"])],
["Average wind chill", "%.2f %s" % (chil_avg, units["temp"])],
["Median wind chill", "%.2f %s" % (chil_median, units["temp"])],
["Range of wind chills", "%.2f %s" % (chil_range, units["temp"])],
["Most common wind chill", "%.2f %s (%d occurrences)" % (chil_mode, units["temp"], chil_mode_count)]
]
return data2
def temp_info(data, units):
""""Gets the temperature info."""
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
temp_median = calculations.median(temp_data)
temp_range = calculations.range(temp_data)
temp_mode, temp_mode_count = calculations.mode(temp_data)
# Create the data list.
data2 = [
["Lowest temperature", "%.2f %s" % (temp_low, units["temp"])],
["Highest temperature", "%.2f %s" % (temp_high, units["temp"])],
["Average temperature", "%.2f %s" % (temp_avg, units["temp"])],
["Median temperature", "%.2f %s" % (temp_median, units["temp"])],
["Range of temperatures", "%.2f %s" % (temp_range, units["temp"])],
["Most common temperature", "%.2f %s (%d occurrences)" % (temp_mode, units["temp"], temp_mode_count)]
]
return data2
def humi_info(data, units):
"""Gets the humidity info."""
# Get the data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
humi_median = calculations.median(humi_data)
humi_range = calculations.range(humi_data)
humi_mode, humi_mode_count = calculations.mode(humi_data)
# Create the data list.
data2 = [["Lowest humidity", "%.2f%%" % humi_low],
["Highest humidity", "%.2f%%" % humi_high],
["Average humidity", "%.2f%%" % humi_avg],
["Median humidity", "%.2f%%" % humi_median],
["Range of humidity", "%.2f%%" % humi_range],
[
"Most common humidity",
"%.2f%% (%d occurrences)" % (humi_mode, humi_mode_count)
]]
return data2
def get_data(data):
"""Gets the graph data."""
# Get the date data.
date_data = datasets.get_column(data, 0)
new_dates = dates.get_datetimes(date_data)
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
chil_data = datasets.convert_float(datasets.get_column(data, 2))
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_data = datasets.convert_float(datasets.none_to_zero(prec_data1))
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data = datasets.convert_float(datasets.none_to_zero(wind_data1))
humi_data = datasets.convert_float(datasets.get_column(data, 5))
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data = datasets.convert_float(airp_data1)
visi_data = datasets.convert_float(datasets.get_column(data, 7))
clou_data1, clou_data2 = datasets.split_list3(datasets.get_column(data, 8))
clou_data2 = datasets.strip_items(clou_data2, ["(", ")"])
prec_split = datasets.split_list2(datasets.get_column(data, 3))
prec_total = 0
prec_total_rain = 0
prec_total_snow = 0
prec_total_hail = 0
prec_total_sleet = 0
prec_none = 0
prec_rain = 0
prec_snow = 0
prec_hail = 0
prec_sleet = 0
for i in prec_split:
if i[1] != "None":
prec_total += float(i[0])
if i[1] == "None":
prec_none += 1
elif i[1] == "Rain":
prec_total_rain += float(i[0])
prec_rain += 1
elif i[1] == "Snow":
prec_total_snow += float(i[0])
prec_snow += 1
elif i[1] == "Hail":
prec_total_hail += float(i[0])
prec_hail += 1
elif i[1] == "Sleet":
prec_total_sleet += float(i[0])
prec_sleet += 1
prec_amount = [
prec_total_rain, prec_total_snow, prec_total_hail, prec_total_sleet
]
prec_days = [prec_none, prec_rain, prec_snow, prec_hail, prec_sleet]
airp_steady = 0
airp_rising = 0
airp_falling = 0
for i in airp_data2:
if i == "Steady":
airp_steady += 1
elif i == "Rising":
airp_rising += 1
elif i == "Falling":
airp_falling += 1
airp_change = [airp_steady, airp_rising, airp_falling]
clou_sunny = 0
clou_msunny = 0
clou_pcloudy = 0
clou_mcloudy = 0
clou_cloudy = 0
for i in clou_data1:
if i == "Sunny":
clou_sunny += 1
elif i == "Mostly Sunny":
clou_msunny += 1
elif i == "Partly Cloudy":
clou_pcloudy += 1
elif i == "Mostly Cloudy":
clou_mcloudy += 1
elif i == "Cloudy":
clou_cloudy += 1
clou_days = [
clou_sunny, clou_msunny, clou_pcloudy, clou_mcloudy, clou_cloudy
]
clou_types = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in clou_data2:
if i == "None":
clou_types[0] += 1
elif i == "Unknown":
clou_types[1] += 1
elif i == "Cirrus":
clou_types[2] += 1
elif i == "Cirrocumulus":
clou_types[3] += 1
elif i == "Cirrostratus":
clou_types[4] += 1
elif i == "Cumulonimbus":
clou_types[5] += 1
elif i == "Altocumulus":
clou_types[6] += 1
elif i == "Altostratus":
clou_types[7] += 1
elif i == "Stratus":
clou_types[8] += 1
elif i == "Cumulus":
clou_types[9] += 1
elif i == "Stratocumulus":
clou_types[10] += 1
data = {
"date_labels": date_data,
"date_ticks": new_dates,
"temp_data": temp_data,
"prec_data": prec_data,
"wind_data": wind_data,
"humi_data": humi_data,
"airp_data": airp_data,
"prec_amount": prec_amount,
"prec_days": prec_days,
"airp_change": airp_change,
"clou_days": clou_days,
"clou_types": clou_types,
"chil_data": chil_data,
"visi_data": visi_data
}
return data
def filter_data(data, condition, insensitive):
"""Filters the data based on the user's conditions."""
# Create the list for the filtered data.
filtered = []
# Get the column of the data that is being filtered.
string_compare = False
col = []
field = condition[0].lower()
if field == "temperature":
col = datasets.convert_float(datasets.get_column(data, 1))
elif field == "wind chill":
col = datasets.convert_float(datasets.get_column(data, 2))
elif field == "precipitation amount":
col = datasets.get_column(data, 3)
col = datasets.convert_float(
datasets.none_to_zero(datasets.split_list(col)[0]))
elif field == "precipitation type":
string_compare = True
ncol = datasets.get_column(data, 3)
col = []
for i in ncol:
if i == "None":
col.append(i)
else:
i_split = i.split(" ")
col.append(i_split[1])
elif field == "wind speed":
col = datasets.get_column(data, 4)
col = datasets.convert_float(
datasets.none_to_zero(datasets.split_list(col)[0]))
elif field == "wind direction":
string_compare = True
ncol = datasets.get_column(data, 4)
col = []
for i in ncol:
if i == "None":
col.append(i)
else:
i_split = i.split(" ")
col.append(i_split[1])
elif field == "humidity":
col = datasets.convert_float(datasets.get_column(data, 5))
elif field == "air pressure":
col = datasets.convert_float(
datasets.split_list(datasets.get_column(data, 6))[0])
elif field == "air pressure change":
string_compare = True
col = datasets.split_list(datasets.get_column(data, 6))[1]
elif field == "visibility":
col = datasets.convert_float(datasets.get_column(data, 7))
elif field == "cloud cover":
string_compare = True
col = datasets.split_list3(datasets.get_column(data, 8))[0]
elif field == "cloud type":
string_compare = True
col = datasets.strip_items(
datasets.split_list3(datasets.get_column(data, 8))[1], ["(", ")"])
elif field == "notes":
string_compare = True
col = datasets.get_column(data, 9)
# Loop through the data, and add it to the filtered list if it matches the condition.
for i in range(0, len(data)):
matches = filter_compare(col[i], condition[1].lower(), condition[2],
string_compare, insensitive)
if matches:
filtered.append(data[i])
return filtered
def clou_info(data, units):
"""Gets the cloud cover info."""
# Get the data.
num_days = len(data)
clou_data1, clou_data2 = datasets.split_list3(datasets.get_column(data, 8))
# Get the number of days of each cloud cover.
clou_cover = Counter(clou_data1)
m_list1 = clou_cover.most_common()
m_dict1 = {}
for i in m_list1:
m_dict1[i[0]] = i[1]
# Get the number of days of each cloud type.
clou_type = Counter(datasets.strip_items(clou_data2, ["(", ")"]))
m_list2 = clou_type.most_common()
m_dict2 = {}
for i in m_list2:
m_dict2[i[0]] = i[1]
# If any of the items don't appear, add dict items for them, with the values set to 0.
if "Sunny" not in m_dict1:
m_dict1["Sunny"] = 0
if "Mostly Sunny" not in m_dict1:
m_dict1["Mostly Sunny"] = 0
if "Partly Cloudy" not in m_dict1:
m_dict1["Partly Cloudy"] = 0
if "Mostly Cloudy" not in m_dict1:
m_dict1["Mostly Cloudy"] = 0
if "Cloudy" not in m_dict1:
m_dict1["Cloudy"] = 0
if "None" not in m_dict2:
m_dict2["None"] = 0
if "Unknown" not in m_dict2:
m_dict2["Unknown"] = 0
if "Cirrus" not in m_dict2:
m_dict2["Cirrus"] = 0
if "Cirrocumulus" not in m_dict2:
m_dict2["Cirrocumulus"] = 0
if "Cirrostratus" not in m_dict2:
m_dict2["Cirrostratus"] = 0
if "Cumulonimbus" not in m_dict2:
m_dict2["Cumulonimbus"] = 0
if "Altocumulus" not in m_dict2:
m_dict2["Altocumulus"] = 0
if "Altostratus" not in m_dict2:
m_dict2["Altostratus"] = 0
if "Stratus" not in m_dict2:
m_dict2["Stratus"] = 0
if "Cumulus" not in m_dict2:
m_dict2["Cumulus"] = 0
if "Stratocumulus" not in m_dict2:
m_dict2["Stratocumulus"] = 0
# Create the data list.
data2 = [
[
"Most common cloud cover",
"%s (%s occurrences)" % (m_list1[0][0], m_list1[0][1])
],
[
"Most common cloud type",
"%s (%s occurrences)" % (m_list2[0][0], m_list2[0][1])
],
[
"Days sunny",
"%s day%s (%d%%)" %
(m_dict1["Sunny"], "" if m_dict1["Sunny"] == 1 else "s",
(m_dict1["Sunny"] / num_days) * 100)
],
[
"Days mostly sunny",
"%s day%s (%d%%)" % (m_dict1["Mostly Sunny"],
"" if m_dict1["Mostly Sunny"] == 1 else "s",
(m_dict1["Mostly Sunny"] / num_days) * 100)
],
[
"Days partly cloudy",
"%s day%s (%d%%)" % (m_dict1["Partly Cloudy"],
"" if m_dict1["Partly Cloudy"] == 1 else "s",
(m_dict1["Partly Cloudy"] / num_days) * 100)
],
[
"Days mostly cloudy",
"%s day%s (%d%%)" % (m_dict1["Mostly Cloudy"],
"" if m_dict1["Mostly Cloudy"] == 1 else "s",
(m_dict1["Mostly Cloudy"] / num_days) * 100)
],
[
"Days cloudy",
"%s day%s (%d%%)" %
(m_dict1["Cloudy"], "" if m_dict1["Cloudy"] == 1 else "s",
(m_dict1["Cloudy"] / num_days) * 100)
],
[
"Days with no clouds",
"%s day%s (%d%%)" %
(m_dict2["None"], "" if m_dict2["None"] == 1 else "s",
(m_dict2["None"] / num_days) * 100)
],
[
"Days with unknown clouds",
"%s day%s (%d%%)" %
(m_dict2["Unknown"], "" if m_dict2["Unknown"] == 1 else "s",
(m_dict2["Unknown"] / num_days) * 100)
],
[
"Days with cirrus",
"%s day%s (%d%%)" %
(m_dict2["Cirrus"], "" if m_dict2["Cirrus"] == 1 else "s",
(m_dict2["Unknown"] / num_days) * 100)
],
[
"Days with cirrocumulus",
"%s day%s (%d%%)" % (m_dict2["Cirrocumulus"],
"" if m_dict2["Cirrocumulus"] == 1 else "s",
(m_dict2["Cirrocumulus"] / num_days) * 100)
],
[
"Days with cirrostratos",
"%s day%s (%d%%)" % (m_dict2["Cirrostratus"],
"" if m_dict2["Cirrostratus"] == 1 else "s",
(m_dict2["Cirrostratus"] / num_days) * 100)
],
[
"Days with cumulonimbus",
"%s day%s (%d%%)" % (m_dict2["Cumulonimbus"],
"" if m_dict2["Cumulonimbus"] == 1 else "s",
(m_dict2["Cumulonimbus"] / num_days) * 100)
],
[
"Days with altocumulus",
"%s day%s (%d%%)" % (m_dict2["Altocumulus"],
"" if m_dict2["Altocumulus"] == 1 else "s",
(m_dict2["Altocumulus"] / num_days) * 100)
],
[
"Days with altostratus",
"%s day%s (%d%%)" % (m_dict2["Altostratus"],
"" if m_dict2["Altostratus"] == 1 else "s",
(m_dict2["Altostratus"] / num_days) * 100)
],
[
"Days with stratus",
"%s day%s (%d%%)" %
(m_dict2["Stratus"], "" if m_dict2["Stratus"] == 1 else "s",
(m_dict2["Stratus"] / num_days) * 100)
],
[
"Days with cumulus",
"%s day%s (%d%%)" %
(m_dict2["Cumulus"], "" if m_dict2["Cumulus"] == 1 else "s",
(m_dict2["Cumulus"] / num_days) * 100)
],
[
"Days with stratocumulus",
"%s day%s (%d%%)" % (m_dict2["Stratocumulus"],
"" if m_dict2["Stratocumulus"] == 1 else "s",
(m_dict2["Stratocumulus"] / num_days) * 100)
]
]
return data2
def get_data(data):
"""Gets the graph data."""
# Get the date data.
date_data = datasets.get_column(data, 0)
new_dates = get_dates(date_data)
# Get the data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
chil_data = datasets.convert_float(datasets.get_column(data, 2))
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_data = datasets.convert_float(datasets.none_to_zero(prec_data1))
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data = datasets.convert_float(datasets.none_to_zero(wind_data1))
humi_data = datasets.convert_float(datasets.get_column(data, 5))
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data = datasets.convert_float(airp_data1)
visi_data = datasets.convert_float(datasets.get_column(data, 7))
clou_data1, clou_data2 = datasets.split_list3(datasets.get_column(data, 8))
clou_data2 = datasets.strip_items(clou_data2, ["(", ")"])
prec_split = datasets.split_list2(datasets.get_column(data, 3))
prec_total = 0
prec_total_rain = 0
prec_total_snow = 0
prec_total_hail = 0
prec_total_sleet = 0
prec_none = 0
prec_rain = 0
prec_snow = 0
prec_hail = 0
prec_sleet = 0
for i in prec_split:
if i[1] != "None":
prec_total += float(i[0])
if i[1] == "None":
prec_none += 1
elif i[1] == "Rain":
prec_total_rain += float(i[0])
prec_rain += 1
elif i[1] == "Snow":
prec_total_snow += float(i[0])
prec_snow += 1
elif i[1] == "Hail":
prec_total_hail += float(i[0])
prec_hail += 1
elif i[1] == "Sleet":
prec_total_sleet += float(i[0])
prec_sleet += 1
prec_amount = [prec_total_rain, prec_total_snow, prec_total_hail, prec_total_sleet]
prec_days = [prec_none, prec_rain, prec_snow, prec_hail, prec_sleet]
airp_steady = 0
airp_rising = 0
airp_falling = 0
for i in airp_data2:
if i == "Steady":
airp_steady += 1
elif i == "Rising":
airp_rising += 1
elif i == "Falling":
airp_falling += 1
airp_change = [airp_steady, airp_rising, airp_falling]
clou_sunny = 0
clou_msunny = 0
clou_pcloudy = 0
clou_mcloudy = 0
clou_cloudy = 0
for i in clou_data1:
if i == "Sunny":
clou_sunny += 1
elif i == "Mostly Sunny":
clou_msunny += 1
elif i == "Partly Cloudy":
clou_pcloudy += 1
elif i == "Mostly Cloudy":
clou_mcloudy += 1
elif i == "Cloudy":
clou_cloudy += 1
clou_days = [clou_sunny, clou_msunny, clou_pcloudy, clou_mcloudy, clou_cloudy]
clou_types = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in clou_data2:
if i == "None":
clou_types[0] += 1
elif i == "Unknown":
clou_types[1] += 1
elif i == "Cirrus":
clou_types[2] += 1
elif i == "Cirrocumulus":
clou_types[3] += 1
elif i == "Cirrostratus":
clou_types[4] += 1
elif i == "Cumulonimbus":
clou_types[5] += 1
elif i == "Altocumulus":
clou_types[6] += 1
elif i == "Altostratus":
clou_types[7] += 1
elif i == "Stratus":
clou_types[8] += 1
elif i == "Cumulus":
clou_types[9] += 1
elif i == "Stratocumulus":
clou_types[10] += 1
data = [date_data, new_dates, temp_data, prec_data, wind_data, humi_data, airp_data, prec_amount,
prec_days, airp_change, clou_days, clou_types, chil_data, visi_data]
return data
def general_info(data, units):
"""Gets the general info."""
# Get the date data.
date_data = datasets.get_column(data, 0)
date_first = date_data[0]
date_last = date_data[len(date_data) - 1]
date_first2 = datetime.datetime.strptime(date_first, "%d/%m/%Y")
date_last2 = datetime.datetime.strptime(date_last, "%d/%m/%Y")
date_num = (date_last2 - date_first2).days + 1
day_num = len(data)
# Get the temperature data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
# Get the wind chill data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
# Get the precipitation data.
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_data1 = datasets.convert_float(datasets.none_to_zero(prec_data1))
try:
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
except:
prec_low = "None"
prec_high = "None"
prec_avg = "None"
# Get the wind data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.convert_float(datasets.none_to_zero(wind_data1))
try:
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
except:
wind_low = "None"
wind_high = "None"
wind_avg = "None"
# Get the humidity data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
# Get the air pressure data.
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
# Get the visibility data.
visi_data = datasets.convert_float(datasets.get_column(data, 7))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
# Get the cloud cover data.
clou_data = datasets.split_list3(datasets.get_column(data, 8))
clou_data1 = Counter(clou_data[0])
clou_data2 = Counter(datasets.strip_items(clou_data[1], ["(", ")"]))
clou_data1_counter = clou_data1.most_common(1)[0]
clou_data2_counter = clou_data2.most_common(1)[0]
clou_mode1 = clou_data1_counter[0]
clou_mode1_count = clou_data1_counter[1]
clou_mode2 = clou_data2_counter[0]
clou_mode2_count = clou_data2_counter[1]
# Change any values, if needed.
prec_low = "None" if prec_low == "None" else ("%.2f %s" % (prec_low, units["prec"]))
prec_high = "None" if prec_high == "None" else ("%.2f %s" % (prec_high, units["prec"]))
prec_avg = "None" if prec_avg == "None" else ("%.2f %s" % (prec_avg, units["prec"]))
wind_low = "None" if wind_low == "None" else ("%.2f %s" % (wind_low, units["wind"]))
wind_high = "None" if wind_high == "None" else ("%.2f %s" % (wind_high, units["wind"]))
wind_avg = "None" if wind_avg == "None" else ("%.2f %s" % (wind_avg, units["wind"]))
# Create the data list.
data2 = [
["First date", "%s" % date_first],
["Last date", "%s" % date_last],
["Number of days", "%d days" % day_num],
["Range of days", "%d days" % date_num],
["Lowest temperature", "%.2f %s" % (temp_low, units["temp"])],
["Highest temperature", "%.2f %s" % (temp_high, units["temp"])],
["Average temperature", "%.2f %s" % (temp_avg, units["temp"])],
["Lowest wind chill", "%.2f %s" % (chil_low, units["temp"])],
["Highest wind chill", "%.2f %s" % (chil_high, units["temp"])],
["Average wind chill", "%.2f %s" % (chil_avg, units["temp"])],
["Lowest precipitation", prec_low],
["Highest precipitation", prec_high],
["Average precipitation", prec_avg],
["Lowest wind speed", wind_low],
["Highest wind speed", wind_high],
["Average wind speed", wind_avg],
["Lowest humidity", "%.2f%%" % humi_low],
["Highest humidity", "%.2f%%" % humi_high],
["Average humidity", "%.2f%%" % humi_avg],
["Lowest air pressure", "%.2f %s" % (airp_low, units["airp"])],
["Highest air pressure", "%.2f %s" % (airp_high, units["airp"])],
["Average air pressure", "%.2f %s" % (airp_avg, units["airp"])],
["Lowest visibility", "%.2f %s" % (visi_low, units["visi"])],
["Highest visibility", "%.2f %s" % (visi_high, units["visi"])],
["Average visibility", "%.2f %s" % (visi_avg, units["visi"])],
["Most common cloud cover", "%s (%d occurrences)" % (clou_mode1, clou_mode1_count)],
["Most common cloud type", "%s (%d occurrences)" % (clou_mode2, clou_mode2_count)]
]
return data2
def prec_info(data, units):
""""Gets the precipitation info."""
# Get the data.
num_days = len(data)
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_split = datasets.split_list2(datasets.get_column(data, 3))
prec_data1 = datasets.none_to_zero(prec_data1)
prec_data1 = datasets.convert_float(prec_data1)
try:
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
prec_median = calculations.median(prec_data1)
prec_range = calculations.range(prec_data1)
except:
prec_low = "None"
prec_high = "None"
prec_avg = "None"
prec_median = "None"
prec_range = "None"
prec_total = 0
prec_total_rain = 0
prec_total_snow = 0
prec_total_hail = 0
prec_total_sleet = 0
prec_none = 0
prec_rain = 0
prec_snow = 0
prec_hail = 0
prec_sleet = 0
for i in prec_split:
if i[1] != "None":
prec_total += float(i[0])
if i[1] == "None":
prec_none += 1
elif i[1] == "Rain":
prec_total_rain += float(i[0])
prec_rain += 1
elif i[1] == "Snow":
prec_total_snow += float(i[0])
prec_snow += 1
elif i[1] == "Hail":
prec_total_hail += float(i[0])
prec_hail += 1
elif i[1] == "Sleet":
prec_total_sleet += float(i[0])
prec_sleet += 1
prec_mode, prec_mode_count = calculations.mode(prec_data2)
if prec_total == 0:
prec_per_rain = "0%"
prec_per_snow = "0%"
prec_per_hail = "0%"
prec_per_sleet = "0%"
else:
prec_per_rain = "%.2f%%" % ((prec_total_rain / prec_total) * 100)
prec_per_snow = "%.2f%%" % ((prec_total_snow / prec_total) * 100)
prec_per_hail = "%.2f%%" % ((prec_total_hail / prec_total) * 100)
prec_per_sleet = "%.2f%%" % ((prec_total_sleet / prec_total) * 100)
# Change any values, if needed.
prec_low = "None" if prec_low == "None" else ("%.2f %s" % (prec_low, units["prec"]))
prec_high = "None" if prec_high == "None" else ("%.2f %s" % (prec_high, units["prec"]))
prec_avg = "None" if prec_avg == "None" else ("%.2f %s" % (prec_avg, units["prec"]))
prec_median = "None" if prec_median == "None" else ("%.2f %s" % (prec_median, units["prec"]))
prec_range = "None" if prec_range == "None" else ("%.2f %s" % (prec_range, units["prec"]))
# Create the data list.
data2 = [
["Lowest precipitation", prec_low],
["Highest precipitation", prec_high],
["Average precipitation", prec_avg],
["Median precipitation", prec_median],
["Range of precipitation", prec_range],
["Total precipitation", "%.2f %s" % (prec_total, units["prec"])],
["Total rain", "%.2f %s (%s)" % (prec_total_rain, units["prec"], prec_per_rain)],
["Total snow", "%.2f %s (%s)" % (prec_total_snow, units["prec"], prec_per_snow)],
["Total hail", "%.2f %s (%s)" % (prec_total_hail, units["prec"], prec_per_hail)],
["Total sleet", "%.2f %s (%s)" % (prec_total_sleet, units["prec"], prec_per_sleet)],
["Days with no precipitation", "%d day%s (%.2f%%)" % (prec_none, "" if prec_none == 1 else "s", (prec_none / num_days) * 100)],
["Days with rain", "%d day%s (%.2f%%)" % (prec_rain, "" if prec_rain == 1 else "s", (prec_rain / num_days) * 100)],
["Days with snow", "%d day%s (%.2f%%)" % (prec_snow, "" if prec_snow == 1 else "s", (prec_snow / num_days) * 100)],
["Days with hail", "%d day%s (%.2f%%)" % (prec_hail, "" if prec_hail == 1 else "s", (prec_hail / num_days) * 100)],
["Days with sleet", "%d day%s (%.2f%%)" % (prec_sleet, "" if prec_sleet == 1 else "s", (prec_sleet / num_days) * 100)],
["Most common precipitation type", "%s (%d occurrences)" % (prec_mode if prec_mode != "" else "None", prec_mode_count)]
]
return data2
def prec_info(data, units):
""""Gets the precipitation info."""
# Get the data.
num_days = len(data)
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_split = datasets.split_list2(datasets.get_column(data, 3))
prec_data1 = datasets.none_to_zero(prec_data1)
prec_data1 = datasets.convert_float(prec_data1)
try:
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
prec_median = calculations.median(prec_data1)
prec_range = calculations.range(prec_data1)
except ZeroDivisionError:
prec_low = "None"
prec_high = "None"
prec_avg = "None"
prec_median = "None"
prec_range = "None"
prec_total = 0
prec_total_rain = 0
prec_total_snow = 0
prec_total_hail = 0
prec_total_sleet = 0
prec_none = 0
prec_rain = 0
prec_snow = 0
prec_hail = 0
prec_sleet = 0
for i in prec_split:
if i[1] != "None":
prec_total += float(i[0])
if i[1] == "None":
prec_none += 1
elif i[1] == "Rain":
prec_total_rain += float(i[0])
prec_rain += 1
elif i[1] == "Snow":
prec_total_snow += float(i[0])
prec_snow += 1
elif i[1] == "Hail":
prec_total_hail += float(i[0])
prec_hail += 1
elif i[1] == "Sleet":
prec_total_sleet += float(i[0])
prec_sleet += 1
prec_mode, prec_mode_count = calculations.mode(prec_data2)
if prec_total == 0:
prec_per_rain = "0%"
prec_per_snow = "0%"
prec_per_hail = "0%"
prec_per_sleet = "0%"
else:
prec_per_rain = "%.2f%%" % ((prec_total_rain / prec_total) * 100)
prec_per_snow = "%.2f%%" % ((prec_total_snow / prec_total) * 100)
prec_per_hail = "%.2f%%" % ((prec_total_hail / prec_total) * 100)
prec_per_sleet = "%.2f%%" % ((prec_total_sleet / prec_total) * 100)
# Change any values, if needed.
prec_low = "None" if prec_low == "None" else ("%.2f %s" %
(prec_low, units["prec"]))
prec_high = "None" if prec_high == "None" else ("%.2f %s" %
(prec_high, units["prec"]))
prec_avg = "None" if prec_avg == "None" else ("%.2f %s" %
(prec_avg, units["prec"]))
prec_median = "None" if prec_median == "None" else (
"%.2f %s" % (prec_median, units["prec"]))
prec_range = "None" if prec_range == "None" else (
"%.2f %s" % (prec_range, units["prec"]))
# Create the data list.
data2 = [
["Lowest precipitation",
prec_low], ["Highest precipitation", prec_high],
["Average precipitation", prec_avg],
["Median precipitation", prec_median],
["Range of precipitation", prec_range],
["Total precipitation",
"%.2f %s" % (prec_total, units["prec"])],
[
"Total rain",
"%.2f %s (%s)" % (prec_total_rain, units["prec"], prec_per_rain)
],
[
"Total snow",
"%.2f %s (%s)" % (prec_total_snow, units["prec"], prec_per_snow)
],
[
"Total hail",
"%.2f %s (%s)" % (prec_total_hail, units["prec"], prec_per_hail)
],
[
"Total sleet",
"%.2f %s (%s)" % (prec_total_sleet, units["prec"], prec_per_sleet)
],
[
"Days with no precipitation",
"%d day%s (%.2f%%)" % (prec_none, "" if prec_none == 1 else "s",
(prec_none / num_days) * 100)
],
[
"Days with rain",
"%d day%s (%.2f%%)" % (prec_rain, "" if prec_rain == 1 else "s",
(prec_rain / num_days) * 100)
],
[
"Days with snow",
"%d day%s (%.2f%%)" % (prec_snow, "" if prec_snow == 1 else "s",
(prec_snow / num_days) * 100)
],
[
"Days with hail",
"%d day%s (%.2f%%)" % (prec_hail, "" if prec_hail == 1 else "s",
(prec_hail / num_days) * 100)
],
[
"Days with sleet",
"%d day%s (%.2f%%)" % (prec_sleet, "" if prec_sleet == 1 else "s",
(prec_sleet / num_days) * 100)
],
[
"Most common precipitation type",
"%s (%d occurrences)" %
(prec_mode if prec_mode != "" else "None", prec_mode_count)
]
]
return data2
def general_info(data, units):
"""Gets the general info."""
# Get the date data.
date_data = datasets.get_column(data, 0)
date_first = date_data[0]
date_last = date_data[len(date_data) - 1]
date_first2 = datetime.datetime.strptime(date_first, "%d/%m/%Y")
date_last2 = datetime.datetime.strptime(date_last, "%d/%m/%Y")
date_num = (date_last2 - date_first2).days + 1
day_num = len(data)
# Get the temperature data.
temp_data = datasets.convert_float(datasets.get_column(data, 1))
temp_low = min(temp_data)
temp_high = max(temp_data)
temp_avg = calculations.mean(temp_data)
# Get the wind chill data.
chil_data = datasets.convert_float(datasets.get_column(data, 2))
chil_low = min(chil_data)
chil_high = max(chil_data)
chil_avg = calculations.mean(chil_data)
# Get the precipitation data.
prec_data1, prec_data2 = datasets.split_list(datasets.get_column(data, 3))
prec_data1 = datasets.convert_float(datasets.none_to_zero(prec_data1))
try:
prec_low = min(prec_data1)
prec_high = max(prec_data1)
prec_avg = calculations.mean(prec_data1)
except ZeroDivisionError:
prec_low = "None"
prec_high = "None"
prec_avg = "None"
# Get the wind data.
wind_data1, wind_data2 = datasets.split_list(datasets.get_column(data, 4))
wind_data1 = datasets.convert_float(datasets.none_to_zero(wind_data1))
try:
wind_low = min(wind_data1)
wind_high = max(wind_data1)
wind_avg = calculations.mean(wind_data1)
except ZeroDivisionError:
wind_low = "None"
wind_high = "None"
wind_avg = "None"
# Get the humidity data.
humi_data = datasets.convert_float(datasets.get_column(data, 5))
humi_low = min(humi_data)
humi_high = max(humi_data)
humi_avg = calculations.mean(humi_data)
# Get the air pressure data.
airp_data1, airp_data2 = datasets.split_list(datasets.get_column(data, 6))
airp_data1 = datasets.convert_float(airp_data1)
airp_low = min(airp_data1)
airp_high = max(airp_data1)
airp_avg = calculations.mean(airp_data1)
# Get the visibility data.
visi_data = datasets.convert_float(datasets.get_column(data, 7))
visi_low = min(visi_data)
visi_high = max(visi_data)
visi_avg = calculations.mean(visi_data)
# Get the cloud cover data.
clou_data = datasets.split_list3(datasets.get_column(data, 8))
clou_data1 = Counter(clou_data[0])
clou_data2 = Counter(datasets.strip_items(clou_data[1], ["(", ")"]))
clou_data1_counter = clou_data1.most_common(1)[0]
clou_data2_counter = clou_data2.most_common(1)[0]
clou_mode1 = clou_data1_counter[0]
clou_mode1_count = clou_data1_counter[1]
clou_mode2 = clou_data2_counter[0]
clou_mode2_count = clou_data2_counter[1]
# Change any values, if needed.
prec_low = "None" if prec_low == "None" else ("%.2f %s" %
(prec_low, units["prec"]))
prec_high = "None" if prec_high == "None" else ("%.2f %s" %
(prec_high, units["prec"]))
prec_avg = "None" if prec_avg == "None" else ("%.2f %s" %
(prec_avg, units["prec"]))
wind_low = "None" if wind_low == "None" else ("%.2f %s" %
(wind_low, units["wind"]))
wind_high = "None" if wind_high == "None" else ("%.2f %s" %
(wind_high, units["wind"]))
wind_avg = "None" if wind_avg == "None" else ("%.2f %s" %
(wind_avg, units["wind"]))
# Create the data list.
data2 = [["First date", "%s" % date_first],
["Last date", "%s" % date_last],
["Number of days", "%d days" % day_num],
["Range of days", "%d days" % date_num],
["Lowest temperature",
"%.2f %s" % (temp_low, units["temp"])],
["Highest temperature",
"%.2f %s" % (temp_high, units["temp"])],
["Average temperature",
"%.2f %s" % (temp_avg, units["temp"])],
["Lowest wind chill",
"%.2f %s" % (chil_low, units["temp"])],
["Highest wind chill",
"%.2f %s" % (chil_high, units["temp"])],
["Average wind chill",
"%.2f %s" % (chil_avg, units["temp"])],
["Lowest precipitation", prec_low],
["Highest precipitation", prec_high],
["Average precipitation", prec_avg],
["Lowest wind speed",
wind_low], ["Highest wind speed", wind_high],
["Average wind speed", wind_avg],
["Lowest humidity", "%.2f%%" % humi_low],
["Highest humidity", "%.2f%%" % humi_high],
["Average humidity", "%.2f%%" % humi_avg],
["Lowest air pressure",
"%.2f %s" % (airp_low, units["airp"])],
["Highest air pressure",
"%.2f %s" % (airp_high, units["airp"])],
["Average air pressure",
"%.2f %s" % (airp_avg, units["airp"])],
["Lowest visibility",
"%.2f %s" % (visi_low, units["visi"])],
["Highest visibility",
"%.2f %s" % (visi_high, units["visi"])],
["Average visibility",
"%.2f %s" % (visi_avg, units["visi"])],
[
"Most common cloud cover",
"%s (%d occurrences)" % (clou_mode1, clou_mode1_count)
],
[
"Most common cloud type",
"%s (%d occurrences)" % (clou_mode2, clou_mode2_count)
]]
return data2
def clou_info(data, units):
"""Gets the cloud cover info."""
# Get the data.
num_days = len(data)
clou_data1, clou_data2 = datasets.split_list3(datasets.get_column(data, 8))
# Get the number of days of each cloud cover.
clou_cover = Counter(clou_data1)
m_list1 = clou_cover.most_common()
m_dict1 = {}
for i in m_list1:
m_dict1[i[0]] = i[1]
# Get the number of days of each cloud type.
clou_type = Counter(datasets.strip_items(clou_data2, ["(", ")"]))
m_list2 = clou_type.most_common()
m_dict2 = {}
for i in m_list2:
m_dict2[i[0]] = i[1]
# If any of the items don't appear, add dict items for them, with the values set to 0.
if not "Sunny" in m_dict1:
m_dict1["Sunny"] = 0
if not "Mostly Sunny" in m_dict1:
m_dict1["Mostly Sunny"] = 0
if not "Partly Cloudy" in m_dict1:
m_dict1["Partly Cloudy"] = 0
if not "Mostly Cloudy" in m_dict1:
m_dict1["Mostly Cloudy"] = 0
if not "Cloudy" in m_dict1:
m_dict1["Cloudy"] = 0
if not "None" in m_dict2:
m_dict2["None"] = 0
if not "Unknown" in m_dict2:
m_dict2["Unknown"] = 0
if not "Cirrus" in m_dict2:
m_dict2["Cirrus"] = 0
if not "Cirrocumulus" in m_dict2:
m_dict2["Cirrocumulus"] = 0
if not "Cirrostratus" in m_dict2:
m_dict2["Cirrostratus"] = 0
if not "Cumulonimbus" in m_dict2:
m_dict2["Cumulonimbus"] = 0
if not "Altocumulus" in m_dict2:
m_dict2["Altocumulus"] = 0
if not "Altostratus" in m_dict2:
m_dict2["Altostratus"] = 0
if not "Stratus" in m_dict2:
m_dict2["Stratus"] = 0
if not "Cumulus" in m_dict2:
m_dict2["Cumulus"] = 0
if not "Stratocumulus" in m_dict2:
m_dict2["Stratocumulus"] = 0
# Create the data list.
data2 = [
["Most common cloud cover", "%s (%s occurrences)" % (m_list1[0][0], m_list1[0][1])],
["Most common cloud type", "%s (%s occurrences)" % (m_list2[0][0], m_list2[0][1])],
["Days sunny", "%s day%s (%d%%)" % (m_dict1["Sunny"], "" if m_dict1["Sunny"] == 1 else "s", (m_dict1["Sunny"] / num_days) * 100)],
["Days mostly sunny", "%s day%s (%d%%)" % (m_dict1["Mostly Sunny"], "" if m_dict1["Mostly Sunny"] == 1 else "s", (m_dict1["Mostly Sunny"] / num_days) * 100)],
["Days partly cloudy", "%s day%s (%d%%)" % (m_dict1["Partly Cloudy"], "" if m_dict1["Partly Cloudy"] == 1 else "s", (m_dict1["Partly Cloudy"] / num_days) * 100)],
["Days mostly cloudy", "%s day%s (%d%%)" % (m_dict1["Mostly Cloudy"], "" if m_dict1["Mostly Cloudy"] == 1 else "s", (m_dict1["Mostly Cloudy"] / num_days) * 100)],
["Days cloudy", "%s day%s (%d%%)" % (m_dict1["Cloudy"], "" if m_dict1["Cloudy"] == 1 else "s", (m_dict1["Cloudy"] / num_days) * 100)],
["Days with no clouds", "%s day%s (%d%%)" % (m_dict2["None"], "" if m_dict2["None"] == 1 else "s", (m_dict2["None"] / num_days) * 100)],
["Days with unknown clouds", "%s day%s (%d%%)" % (m_dict2["Unknown"], "" if m_dict2["Unknown"] == 1 else "s", (m_dict2["Unknown"] / num_days) * 100)],
["Days with cirrus", "%s day%s (%d%%)" % (m_dict2["Cirrus"], "" if m_dict2["Cirrus"] == 1 else "s", (m_dict2["Unknown"] / num_days) * 100)],
["Days with cirrocumulus", "%s day%s (%d%%)" % (m_dict2["Cirrocumulus"], "" if m_dict2["Cirrocumulus"] == 1 else "s", (m_dict2["Cirrocumulus"] / num_days) * 100)],
["Days with cirrostratos", "%s day%s (%d%%)" % (m_dict2["Cirrostratus"], "" if m_dict2["Cirrostratus"] == 1 else "s", (m_dict2["Cirrostratus"] / num_days) * 100)],
["Days with cumulonimbus", "%s day%s (%d%%)" % (m_dict2["Cumulonimbus"], "" if m_dict2["Cumulonimbus"] == 1 else "s", (m_dict2["Cumulonimbus"] / num_days) * 100)],
["Days with altocumulus", "%s day%s (%d%%)" % (m_dict2["Altocumulus"], "" if m_dict2["Altocumulus"] == 1 else "s", (m_dict2["Altocumulus"] / num_days) * 100)],
["Days with altostratus", "%s day%s (%d%%)" % (m_dict2["Altostratus"], "" if m_dict2["Altostratus"] == 1 else "s", (m_dict2["Altostratus"] / num_days) * 100)],
["Days with stratus", "%s day%s (%d%%)" % (m_dict2["Stratus"], "" if m_dict2["Stratus"] == 1 else "s", (m_dict2["Stratus"] / num_days) * 100)],
["Days with cumulus", "%s day%s (%d%%)" % (m_dict2["Cumulus"], "" if m_dict2["Cumulus"] == 1 else "s", (m_dict2["Cumulus"] / num_days) * 100)],
["Days with stratocumulus", "%s day%s (%d%%)" % (m_dict2["Stratocumulus"], "" if m_dict2["Stratocumulus"] == 1 else "s", (m_dict2["Stratocumulus"] / num_days) * 100)]
]
return data2
def filter_data(data, condition):
"""Filters the data based on the user's conditions."""
# Create the list for the filtered data.
filtered = []
# Get the column of the data that is being filtered.
string_compare = False
col = []
field = condition[0].lower()
if field == "temperature":
col = datasets.convert_float(datasets.get_column(data, 1))
elif field == "wind chill":
col = datasets.convert_float(datasets.get_column(data, 2))
elif field == "precipitation amount":
col = datasets.get_column(data, 3)
col = datasets.convert_float(datasets.none_to_zero(datasets.split_list(col)[0]))
elif field == "precipitation type":
string_compare = True
ncol = datasets.get_column(data, 3)
col = []
for i in ncol:
if i == "None":
col.append(i)
else:
i_split = i.split(" ")
col.append(i_split[1])
elif field == "wind speed":
col = datasets.get_column(data, 4)
col = datasets.convert_float(datasets.none_to_zero(datasets.split_list(col)[0]))
elif field == "wind direction":
string_compare = True
ncol = datasets.get_column(data, 4)
col = []
for i in ncol:
if i == "None":
col.append(i)
else:
i_split = i.split(" ")
col.append(i_split[1])
elif field == "humidity":
col = datasets.convert_float(datasets.get_column(data, 5))
elif field == "air pressure":
col = datasets.convert_float(datasets.split_list(datasets.get_column(data, 6))[0])
elif field == "air pressure change":
string_compare = True
col = datasets.split_list(datasets.get_column(data, 6))[1]
elif field == "visibility":
col = datasets.convert_float(datasets.get_column(data, 7))
elif field == "cloud cover":
string_compare = True
col = datasets.split_list3(datasets.get_column(data, 8))[0]
elif field == "cloud type":
string_compare = True
col = datasets.strip_items(datasets.split_list3(datasets.get_column(data, 8))[1], ["(", ")"])
elif field == "notes":
string_compare = True
col = datasets.get_column(data, 9)
# Loop through the data, and add it to the filtered list if it matches the condition.
for i in range(0, len(data)):
matches = filter_compare(col[i], condition[1].lower(), condition[2], string_compare)
if matches:
filtered.append(data[i])
return filtered