diff --git a/data analysis/analysis/analysis-better.py b/data analysis/analysis/analysis-better.py
new file mode 100644
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+++ b/data analysis/analysis/analysis-better.py	
@@ -0,0 +1,929 @@
+# Titan Robotics Team 2022: Data Analysis Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this should be imported as a python module using 'import analysis'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+# number of easter eggs: 2
+# setup:
+
+__version__ = "1.0.9.000"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.9.000:
+    - refactored
+    - numpyed everything
+1.0.8.005:
+    - minor fixes
+1.0.8.004:
+    - removed a few unused dependencies
+1.0.8.003:
+    - added p_value function
+1.0.8.002:
+    - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+1.0.8.001:
+    - refactors
+    - bugfixes
+1.0.8.000:
+    - depreciated histo_analysis_old
+    - depreciated debug
+    - altered basic_analysis to take array data instead of filepath
+    - refactor
+    - optimization
+1.0.7.002:
+    - bug fixes
+1.0.7.001:
+    - bug fixes
+1.0.7.000:
+    - added tanh_regression (logistical regression)
+    - bug fixes
+1.0.6.005:
+    - added z_normalize function to normalize dataset
+    - bug fixes
+1.0.6.004:
+    - bug fixes
+1.0.6.003:
+    - bug fixes
+1.0.6.002:
+    - bug fixes
+1.0.6.001:
+    - corrected __all__ to contain all of the functions
+1.0.6.000:
+    - added calc_overfit, which calculates two measures of overfit, error and performance
+    - added calculating overfit to optimize_regression
+1.0.5.000:
+    - added optimize_regression function, which is a sample function to find the optimal regressions
+    - optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
+    - planned addition: overfit detection in the optimize_regression function
+1.0.4.002:
+    - added __changelog__
+    - updated debug function with log and exponential regressions
+1.0.4.001:
+    - added log regressions
+    - added exponential regressions
+    - added log_regression and exp_regression to __all__
+1.0.3.008:
+    - added debug function to further consolidate functions
+1.0.3.007:
+    - added builtin benchmark function
+    - added builtin random (linear) data generation function
+    - added device initialization (_init_device)
+1.0.3.006:
+    - reorganized the imports list to be in alphabetical order
+    - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
+1.0.3.005:
+    - major bug fixes
+    - updated historical analysis
+    - depreciated old historical analysis
+1.0.3.004:
+    - added __version__, __author__, __all__
+    - added polynomial regression
+    - added root mean squared function
+    - added r squared function
+1.0.3.003:
+    - bug fixes
+    - added c_entities
+1.0.3.002:
+    - bug fixes
+    - added nc_entities, obstacles, objectives
+    - consolidated statistics.py to analysis.py
+1.0.3.001:
+    - compiled 1d, column, and row basic stats into basic stats function
+1.0.3.000:
+    - added historical analysis function
+1.0.2.xxx:
+    - added z score test
+1.0.1.xxx:
+    - major bug fixes
+1.0.0.xxx:
+    - added loading csv
+    - added 1d, column, row basic stats
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+)
+
+__all__ = [
+    '_init_device',
+    'c_entities',
+    'nc_entities',
+    'obstacles',
+    'objectives',
+    'load_csv',
+    'basic_stats',
+    'z_score',
+    'z_normalize',
+    'stdev_z_split',
+    'histo_analysis',
+    'poly_regression',
+    'log_regression',
+    'exp_regression',
+    'r_squared',
+    'rms',
+    'calc_overfit',
+    'strip_data',
+    'optimize_regression',
+    'select_best_regression',
+    'basic_analysis',
+    # all statistics functions left out due to integration in other functions
+]
+
+# now back to your regularly scheduled programming:
+
+# imports (now in alphabetical order! v 1.0.3.006):
+
+from bisect import bisect_left, bisect_right
+import collections
+import csv
+from decimal import Decimal
+import functools
+from fractions import Fraction
+from itertools import groupby
+import math
+import matplotlib
+import numbers
+import numpy as np
+import pandas
+import random
+import scipy
+from scipy.optimize import curve_fit
+from scipy import stats
+from sklearn import *
+# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
+import time
+import torch
+
+
+class error(ValueError):
+    pass
+
+
+def _init_device(setting, arg):  # initiates computation device for ANNs
+    if setting == "cuda":
+        try:
+            return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu")
+        except:
+            raise error("could not assign cuda or cpu")
+    elif setting == "cpu":
+        try:
+            return torch.device("cpu")
+        except:
+            raise error("could not assign cpu")
+    else:
+        raise error("specified device does not exist")
+
+def load_csv(filepath):
+    with open(filepath, newline='') as csvfile:
+        file_array = list(csv.reader(csvfile))
+        csvfile.close()
+    return file_array
+
+
+# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
+def basic_stats(data, method, arg):
+
+    if method == 'debug':
+        return "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
+
+    if method == "1d" or method == 0:
+
+        data_t = []
+
+        for i in range(0, len(data), 1):
+            data_t.append(float(data[i]))
+
+        _mean = mean(data_t)
+        _median = median(data_t)
+        try:
+            _mode = mode(data_t)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(data_t)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(data_t)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+
+    elif method == "column" or method == 1:
+
+        c_data = []
+        c_data_sorted = []
+
+        for i in data:
+            try:
+                c_data.append(float(i[arg]))
+            except:
+                pass
+
+        _mean = mean(c_data)
+        _median = median(c_data)
+        try:
+            _mode = mode(c_data)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(c_data)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(c_data)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+
+    elif method == "row" or method == 2:
+
+        r_data = []
+
+        for i in range(len(data[arg])):
+            r_data.append(float(data[arg][i]))
+
+        _mean = mean(r_data)
+        _median = median(r_data)
+        try:
+            _mode = mode(r_data)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(r_data)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(r_data)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+
+    else:
+        raise error("method error")
+
+
+# returns z score with inputs of point, mean and standard deviation of spread
+def z_score(point, mean, stdev):
+    score = (point - mean) / stdev
+    return score
+
+
+# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized
+def z_normalize(x, y, mode):
+
+    x_norm = []
+    y_norm = []
+
+    mean = 0
+    stdev = 0
+
+    if mode == 'x':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
+
+        for i in range(0, len(x), 1):
+            x_norm.append(z_score(x[i], _mean, _stdev))
+
+        return x_norm, y
+
+    if mode == 'y':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
+
+        for i in range(0, len(y), 1):
+            y_norm.append(z_score(y[i], _mean, _stdev))
+
+        return x, y_norm
+
+    if mode == 'both':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
+
+        for i in range(0, len(x), 1):
+            x_norm.append(z_score(x[i], _mean, _stdev))
+
+        _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
+
+        for i in range(0, len(y), 1):
+            y_norm.append(z_score(y[i], _mean, _stdev))
+
+        return x_norm, y_norm
+
+    else:
+
+        return error('method error')
+
+
+# returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
+def stdev_z_split(mean, stdev, delta, low_bound, high_bound):
+
+    z_split = []
+    i = low_bound
+
+    while True:
+        z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) *
+                             math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
+        i = i + delta
+        if i > high_bound:
+            break
+
+    return z_split
+
+
+def histo_analysis(hist_data, delta, low_bound, high_bound):
+
+    if hist_data == 'debug':
+        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
+
+    derivative = []
+
+    for i in range(0, len(hist_data), 1):
+        try:
+            derivative.append(float(hist_data[i - 1]) - float(hist_data[i]))
+        except:
+            pass
+
+    derivative_sorted = sorted(derivative, key=int)
+    mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
+    stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]
+
+    predictions = []
+    pred_change = 0
+
+    i = low_bound
+
+    while True:
+        if i > high_bound:
+            break
+
+        try:
+            pred_change = mean_derivative + i * stdev_derivative
+        except:
+            pred_change = mean_derivative
+
+        predictions.append(float(hist_data[-1:][0]) + pred_change)
+
+        i = i + delta
+
+    return predictions
+
+
+def poly_regression(x, y, power):
+
+    if x == "null":  # if x is 'null', then x will be filled with integer points between 1 and the size of y
+        x = []
+
+        for i in range(len(y)):
+            print(i)
+            x.append(i + 1)
+
+    reg_eq = scipy.polyfit(x, y, deg=power)
+    eq_str = ""
+
+    for i in range(0, len(reg_eq), 1):
+        if i < len(reg_eq) - 1:
+            eq_str = eq_str + str(reg_eq[i]) + \
+                "*(z**" + str(len(reg_eq) - i - 1) + ")+"
+        else:
+            eq_str = eq_str + str(reg_eq[i]) + \
+                "*(z**" + str(len(reg_eq) - i - 1) + ")"
+
+    vals = []
+
+    for i in range(0, len(x), 1):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return [eq_str, _rms, r2_d2]
+
+
+def log_regression(x, y, base):
+
+    x_fit = []
+
+    for i in range(len(x)):
+        try:
+            # change of base for logs
+            x_fit.append(np.log(x[i]) / np.log(base))
+        except:
+            pass
+
+    # y = reg_eq[0] * log(x, base) + reg_eq[1]
+    reg_eq = np.polyfit(x_fit, y, 1)
+    q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + \
+        str(base) + "))+" + str(reg_eq[1])
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def exp_regression(x, y, base):
+
+    y_fit = []
+
+    for i in range(len(y)):
+        try:
+            # change of base for logs
+            y_fit.append(np.log(y[i]) / np.log(base))
+        except:
+            pass
+
+    # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
+    reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit))
+    eq_str = "(" + str(base) + "**(" + \
+        str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def tanh_regression(x, y):
+
+    def tanh(x, a, b, c, d):
+
+        return a * np.tanh(b * (x - c)) + d
+
+    reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
+    eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + \
+        "*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def r_squared(predictions, targets):  # assumes equal size inputs
+
+    return metrics.r2_score(np.array(targets), np.array(predictions))
+
+
+def rms(predictions, targets):  # assumes equal size inputs
+
+    _sum = 0
+
+    for i in range(0, len(targets), 1):
+        _sum = (targets[i] - predictions[i]) ** 2
+
+    return float(math.sqrt(_sum / len(targets)))
+
+
+def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
+
+    # performance overfit = performance(train) - performance(test) where performance is r^2
+    # error overfit = error(train) - error(test) where error is rms; biased towards smaller values
+
+    vals = []
+
+    for i in range(0, len(x_test), 1):
+
+        z = x_test[i]
+
+        exec("vals.append(" + equation + ")")
+
+    r2_test = r_squared(vals, y_test)
+    rms_test = rms(vals, y_test)
+
+    return r2_train - r2_test
+
+
+def strip_data(data, mode):
+
+    if mode == "adam":  # x is the row number, y are the data
+        pass
+
+    if mode == "eve":  # x are the data, y is the column number
+        pass
+
+    else:
+        raise error("mode error")
+
+
+# _range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
+def optimize_regression(x, y, _range, resolution):
+    # usage not: for demonstration purpose only, performance is shit
+    if type(resolution) != int:
+        raise error("resolution must be int")
+
+    x_train = x
+    y_train = []
+
+    for i in range(len(y)):
+        y_train.append(float(y[i]))
+
+    x_test = []
+    y_test = []
+
+    for i in range(0, math.floor(len(x) * 0.5), 1):
+        index = random.randint(0, len(x) - 1)
+
+        x_test.append(x[index])
+        y_test.append(float(y[index]))
+
+        x_train.pop(index)
+        y_train.pop(index)
+
+    #print(x_train, x_test)
+    #print(y_train, y_test)
+
+    eqs = []
+    rmss = []
+    r2s = []
+
+    for i in range(0, _range + 1, 1):
+        try:
+            x, y, z = poly_regression(x_train, y_train, i)
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    for i in range(1, 100 * resolution + 1):
+        try:
+            x, y, z = exp_regression(x_train, y_train, float(i / resolution))
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    for i in range(1, 100 * resolution + 1):
+        try:
+            x, y, z = log_regression(x_train, y_train, float(i / resolution))
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    try:
+        x, y, z = tanh_regression(x_train, y_train)
+
+        eqs.append(x)
+        rmss.append(y)
+        r2s.append(z)
+    except:
+        pass
+
+    # marks all equations where r2 = 1 as they 95% of the time overfit the data
+    for i in range(0, len(eqs), 1):
+        if r2s[i] == 1:
+            eqs[i] = ""
+            rmss[i] = ""
+            r2s[i] = ""
+
+    while True:  # removes all equations marked for removal
+        try:
+            eqs.remove('')
+            rmss.remove('')
+            r2s.remove('')
+        except:
+            break
+
+    overfit = []
+
+    for i in range(0, len(eqs), 1):
+
+        overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
+
+    return eqs, rmss, r2s, overfit
+
+
+def select_best_regression(eqs, rmss, r2s, overfit, selector):
+
+    b_eq = ""
+    b_rms = 0
+    b_r2 = 0
+    b_overfit = 0
+
+    ind = 0
+
+    if selector == "min_overfit":
+
+        ind = np.argmin(overfit)
+
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
+
+    if selector == "max_r2s":
+
+        ind = np.argmax(r2s)
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
+
+    return b_eq, b_rms, b_r2, b_overfit
+
+
+def p_value(x, y):  # takes 2 1d arrays
+
+    return stats.ttest_ind(x, y)[1]
+
+
+# assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
+def basic_analysis(data):
+
+    row = len(data)
+    column = []
+
+    for i in range(0, row, 1):
+        column.append(len(data[i]))
+
+    column_max = max(column)
+    row_b_stats = []
+    row_histo = []
+
+    for i in range(0, row, 1):
+        row_b_stats.append(basic_stats(data, "row", i))
+        row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449))
+
+    column_b_stats = []
+
+    for i in range(0, column_max, 1):
+        column_b_stats.append(basic_stats(data, "column", i))
+
+    return[row_b_stats, column_b_stats, row_histo]
+
+
+def benchmark(x, y):
+
+    start_g = time.time()
+    generate_data("data/data.csv", x, y, -10, 10)
+    end_g = time.time()
+
+    start_a = time.time()
+    basic_analysis("data/data.csv")
+    end_a = time.time()
+
+    return [(end_g - start_g), (end_a - start_a)]
+
+
+def generate_data(filename, x, y, low, high):
+
+    file = open(filename, "w")
+
+    for i in range(0, y, 1):
+        temp = ""
+
+        for j in range(0, x - 1, 1):
+            temp = str(random.uniform(low, high)) + "," + temp
+
+        temp = temp + str(random.uniform(low, high))
+        file.write(temp + "\n")
+
+
+class StatisticsError(ValueError):
+    pass
+
+
+def _sum(data, start=0):
+    count = 0
+    n, d = _exact_ratio(start)
+    partials = {d: n}
+    partials_get = partials.get
+    T = _coerce(int, type(start))
+    for typ, values in groupby(data, type):
+        T = _coerce(T, typ)  # or raise TypeError
+        for n, d in map(_exact_ratio, values):
+            count += 1
+            partials[d] = partials_get(d, 0) + n
+    if None in partials:
+
+        total = partials[None]
+        assert not _isfinite(total)
+    else:
+
+        total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
+    return (T, total, count)
+
+
+def _isfinite(x):
+    try:
+        return x.is_finite()  # Likely a Decimal.
+    except AttributeError:
+        return math.isfinite(x)  # Coerces to float first.
+
+
+def _coerce(T, S):
+
+    assert T is not bool, "initial type T is bool"
+
+    if T is S:
+        return T
+
+    if S is int or S is bool:
+        return T
+    if T is int:
+        return S
+
+    if issubclass(S, T):
+        return S
+    if issubclass(T, S):
+        return T
+
+    if issubclass(T, int):
+        return S
+    if issubclass(S, int):
+        return T
+
+    if issubclass(T, Fraction) and issubclass(S, float):
+        return S
+    if issubclass(T, float) and issubclass(S, Fraction):
+        return T
+
+    msg = "don't know how to coerce %s and %s"
+    raise TypeError(msg % (T.__name__, S.__name__))
+
+
+def _exact_ratio(x):
+
+    try:
+
+        if type(x) is float or type(x) is Decimal:
+            return x.as_integer_ratio()
+        try:
+
+            return (x.numerator, x.denominator)
+        except AttributeError:
+            try:
+
+                return x.as_integer_ratio()
+            except AttributeError:
+
+                pass
+    except (OverflowError, ValueError):
+
+        assert not _isfinite(x)
+        return (x, None)
+    msg = "can't convert type '{}' to numerator/denominator"
+    raise TypeError(msg.format(type(x).__name__))
+
+
+def _convert(value, T):
+
+    if type(value) is T:
+
+        return value
+    if issubclass(T, int) and value.denominator != 1:
+        T = float
+    try:
+
+        return T(value)
+    except TypeError:
+        if issubclass(T, Decimal):
+            return T(value.numerator) / T(value.denominator)
+        else:
+            raise
+
+
+def _counts(data):
+
+    table = collections.Counter(iter(data)).most_common()
+    if not table:
+        return table
+
+    maxfreq = table[0][1]
+    for i in range(1, len(table)):
+        if table[i][1] != maxfreq:
+            table = table[:i]
+            break
+    return table
+
+
+def _find_lteq(a, x):
+
+    i = bisect_left(a, x)
+    if i != len(a) and a[i] == x:
+        return i
+    raise ValueError
+
+
+def _find_rteq(a, l, x):
+
+    i = bisect_right(a, x, lo=l)
+    if i != (len(a) + 1) and a[i - 1] == x:
+        return i - 1
+    raise ValueError
+
+
+def _fail_neg(values, errmsg='negative value'):
+
+    for x in values:
+        if x < 0:
+            raise StatisticsError(errmsg)
+        yield x
+
+
+def mean(data):
+
+    if iter(data) is data:
+        data = list(data)
+    n = len(data)
+    if n < 1:
+        raise StatisticsError('mean requires at least one data point')
+    T, total, count = _sum(data)
+    assert count == n
+    return _convert(total / n, T)
+
+
+def median(data):
+
+    data = sorted(data)
+    n = len(data)
+    if n == 0:
+        raise StatisticsError("no median for empty data")
+    if n % 2 == 1:
+        return data[n // 2]
+    else:
+        i = n // 2
+        return (data[i - 1] + data[i]) / 2
+
+
+def mode(data):
+
+    table = _counts(data)
+    if len(table) == 1:
+        return table[0][0]
+    elif table:
+        raise StatisticsError(
+            'no unique mode; found %d equally common values' % len(table)
+        )
+    else:
+        raise StatisticsError('no mode for empty data')
+
+
+def _ss(data, c=None):
+
+    if c is None:
+        c = mean(data)
+    T, total, count = _sum((x - c)**2 for x in data)
+
+    U, total2, count2 = _sum((x - c) for x in data)
+    assert T == U and count == count2
+    total -= total2**2 / len(data)
+    assert not total < 0, 'negative sum of square deviations: %f' % total
+    return (T, total)
+
+
+def variance(data, xbar=None):
+
+    if iter(data) is data:
+        data = list(data)
+    n = len(data)
+    if n < 2:
+        raise StatisticsError('variance requires at least two data points')
+    T, ss = _ss(data, xbar)
+    return _convert(ss / (n - 1), T)
+
+
+def stdev(data, xbar=None):
+
+    var = variance(data, xbar)
+    try:
+        return var.sqrt()
+    except AttributeError:
+        return math.sqrt(var)