def get_baseline_stats(keys):
'''
get raw baseline values of multiple parameters.
:param keys: list
parameters to be included.
:return:
'''
# list of raw data sets (for inspection)
raw_sets = []
# lists to store statistics from every data_set
data_sets_means = []
data_sets_err = []
significance_list = []
for key in keys:
# get the data
raw = raw_data.get_raw_data(key)['c']
# exclude week days
week_ends = [day for day in raw.index if day in [0, 7, 14, 21, 28]]
raw = raw.loc[week_ends]
# transpose, stack, drop irrelevant levels
transposed = raw.T
stacked = transposed.stack()
raw = stacked.droplevel(['replicate', 'days'])
# rename resulting Series
raw.name = key
# append to raw_sets list
raw_sets.append(raw)
# compute statistics
group_by_soil = raw.groupby(raw.index)
means = group_by_soil.mean()
std_err = group_by_soil.sem()
# compute significance and rename
booleans = significance.get_significance_booleans(raw)
annotations = significance.annotate(booleans)
annotations.name = key
# append statistics to lists
data_sets_means.append(means)
data_sets_err.append(std_err)
significance_list.append(annotations)
# combine stats from all data sets into Dataframe
baseline_means = pandas.concat(data_sets_means, axis=1)
baseline_std_errors = pandas.concat(data_sets_err, axis=1)
baseline_significance = pandas.concat(significance_list, axis=1)
return Stats(means=baseline_means,stde=baseline_std_errors,),\
baseline_significance,\
raw_sets
def stack_data(raw_data, normalize_by=None, treatment: str = None):
'''
stack raw data .
parameters
----------
raw_data: str or DataFrame
if a string is given it will be used as an argument for get_raw_data()..
normalize_by: function, optional
how to normalize the data.
treatment: str, optional
can be either "t" (use data from treated samples) or "c" (use data from control samples)
returns
-------
stacked_data_set: Series
Series.index are the group id's for regression
and Series.values are the results being regressed.
Series.name is the name of the data set.
'''
# data
data = get_raw_data(raw_data) if type(raw_data) == str else raw_data
data = (normalize_by(data)
if normalize_by else data[treatment] if treatment else data)
# stack
columns_level_names = data.columns.names
stacked_data_set = data.stack(columns_level_names)
return stacked_data_set
def combine_raw_data(data_set_name, ):
'''
combine raw_data from all LTTs.
:param data_set_name: str
which data set to use.
:param treatment: str
't' for treated samples, 'c' for control samples
:return: combined_raw_data: Series
index is sampling event.
'''
def drop_outliers(data, drop_index):
is_respiration_data = data.name == 'Resp'
if is_respiration_data:
data.drop(drop_index, inplace=True)
# get raw data
combined_raw = get_raw_data(data_set_name)['t']
# stack and drop 'soil' and 'replicate' levels
columns_levels = combined_raw.columns.names
combined_raw = combined_raw.stack(columns_levels).droplevel(columns_levels)
# name raw data
combined_raw.name = data_set_name
# # drop outlying data
# indices_to_drop = [0.17, 7, 7.17]
# drop_outliers(combined_raw, indices_to_drop)
# # insert initial control values for respiration data of treated samples
# if data_set_name == 'Resp' and treatment == 't':
# # get data
# control_raw = get_raw_data(data_set_name)['c']
# # first sampling raw data
# first_row = control_raw.iloc[0]
# # drop all index levels
# first_row.reset_index(drop=True)
# # set all index labels to 0
# new_index = [0 for i in first_row.index]
# first_row.index = new_index
# # insert control first sampling data as the first value of treated samples data
# raw = first_row.append(raw)
return combined_raw
def get_carbon_efficiency(treatment):
wknds = [0, 7, 14, 21]
# raw data
raw_mbc = get_raw_data('MBC')[treatment]
raw_mbc = raw_mbc.loc[wknds] # start-finish of first 3 weeks
# get stats
mbc_stats = get_stats(raw_mbc)
mbc_means = mbc_stats.means
mbc_errors = mbc_stats.stde
# weekly change
weekly_mbc_change = mbc_means.diff()
weekly_mbc_change = weekly_mbc_change.shift(-1).drop(21)
# associated errors for weekly change
errors_squared = mbc_errors**2
add_errors = errors_squared.add(errors_squared.shift(1))
square_root = add_errors**0.5
error_mbc_change = square_root.shift(-1).drop(21)
# weekly respiration stats
weekly_respiration_stats = get_weekly_respiration(treatment)
weekly_respiration = weekly_respiration_stats.means
repiration_error = weekly_respiration_stats.stde
# impose same index for MBC and Respiration data
index = weekly_respiration.index
weekly_mbc_change.index = index
error_mbc_change.index = index
# assimilation-to-consumption ratio (CUE)
CUE = weekly_mbc_change / (weekly_respiration + weekly_mbc_change)
# error propogation
growth_relative_err = error_mbc_change / weekly_mbc_change
respiration_relative_err = repiration_error / weekly_respiration
CUE_error = propagate_error(CUE, respiration_relative_err,
growth_relative_err)
return Stats(
means=CUE,
stde=CUE_error,
)
def get_raw_ltt(
data_set_name,
ltt,
):
'''
get treated samples raw data, for a given LTT.
:param data_set_name:
:param ltt:
:return:
'''
raw_data = get_raw_data(data_set_name)['t'] # treated samples
raw_ltt = raw_data[ltt]
# stack and drop 'replicate' level
stacked = raw_ltt.stack()
droped_n_stacked = stacked.droplevel('replicate')
raw_ltt = droped_n_stacked
raw_ltt.name = data_set_name
return raw_ltt
# drop irrelevant days
t = drop_days(t, days_to_drop) if days_to_drop else t
data = data.loc[t]
# dependent variable abd weights
y = data['mean'].values
error = data['error'].values
# intialize model and perform fit
fit = model.fit(y, t=t, weights=error, nan_policy='omit')
return fit
if __name__ == '__main__':
data_set_name = 'MBC'
soil = 'MIN'
raw = get_raw_data(data_set_name)
raw = baseline_normalize(raw)[soil]
mean = raw.T.mean()
sem = raw.T.sem()
mean[0] = 0
sem[0] = 0
mean.name = 'mean'
sem.name = 'error'
data = pandas.concat([mean, sem], axis=1)
data.dropna(how='any', inplace=True)
fit = fit(data, weekly_growth_decay)
# todo compute stde
from pandas import DataFrame, MultiIndex
from data.raw_data import get_raw_data
from data.stats import get_stats
from data.helpers import *
SOILS = Constants.LTTs
#raw data
RAW_DATA = get_raw_data('Resp')
# limits of time intervals between samplings
timepoints = RAW_DATA.index.values
n_intervals = len(timepoints) - 1
INTERVALS_LIST = [
[timepoints[i], timepoints[i + 1]] for i in range(n_intervals)
] # a list of intervals start and end (i.e [0, 2] for the interval between incubation start and 2 h)
intervals_arrayed = numpy.asarray(INTERVALS_LIST)
intervals = intervals_arrayed.T # array.shape-->(2, len(SAMPLING_TIMEPOINTS))
BEGININGS = intervals[0]
ENDINGS = intervals[1]
SAMPLING_INTERVALS = ENDINGS - BEGININGS
def get_mean_rates(treatment):
'''
get average rate between every two consecutive sampling points.
:param treatment: str
