@@ -146,7 +146,7 @@ def fv(rate, nper, pmt, pv, when='end'):
146146 5% (annually) compounded monthly?
147147
148148 >>> npf.fv(0.05/12, 10*12, -100, -100)
149- 15692.928894335748
149+ 15692.92889433575
150150
151151 By convention, the negative sign represents cash flow out (i.e. money not
152152 available today). Thus, saving $100 a month at 5% annual interest leads
@@ -157,7 +157,7 @@ def fv(rate, nper, pmt, pv, when='end'):
157157
158158 >>> a = np.array((0.05, 0.06, 0.07))/12
159159 >>> npf.fv(a, 10*12, -100, -100)
160- array([ 15692.92889434, 16569.87435405, 17509.44688102]) # may vary
160+ array([15692.92889434, 16569.87435405, 17509.44688102])
161161
162162 """
163163 when = _convert_when (when )
@@ -327,9 +327,9 @@ def nper(rate, pmt, pv, fv=0, when='end'):
327327 ... 8000 : 9001 : 1000]))
328328 array([[[ 64.07334877, 74.06368256],
329329 [108.07548412, 127.99022654]],
330+ <BLANKLINE>
330331 [[ 66.12443902, 76.87897353],
331332 [114.70165583, 137.90124779]]])
332-
333333 """
334334 when = _convert_when (when )
335335 rate , pmt , pv , fv , when = np .broadcast_arrays (rate , pmt , pv , fv , when )
@@ -592,7 +592,7 @@ def pv(rate, nper, pmt, fv=0, when='end'):
592592
593593 >>> a = np.array((0.05, 0.04, 0.03))/12
594594 >>> npf.pv(a, 10*12, -100, 15692.93)
595- array([ -100.00067132, -649.26771385, -1273.78633713]) # may vary
595+ array([ -100.00067132, -649.26771385, -1273.78633713])
596596
597597 So, to end up with the same $15692.93 under the same $100 per month
598598 "savings plan," for annual interest rates of 4% and 3%, one would
@@ -931,7 +931,7 @@ def npv(rate, values):
931931 net present value:
932932
933933 >>> rate, cashflows = 0.08, [-40_000, 5_000, 8_000, 12_000, 30_000]
934- >>> npf.npv(rate, cashflows).round( 5)
934+ >>> np.round( npf.npv(rate, cashflows), 5)
935935 3065.22267
936936
937937 It may be preferable to split the projected cashflow into an initial
@@ -1061,10 +1061,15 @@ def mirr(values, finance_rate, reinvest_rate, *, raise_exceptions=False):
10611061 Finally, let's explore the situation where all cash flows are positive,
10621062 and the `raise_exceptions` parameter is set to True.
10631063
1064- >>> npf.mirr([100, 50, 60, 70], 0.10, 0.12, raise_exceptions=True)
1065- NoRealSolutionError: No real solution exists for MIRR since all
1066- cashflows are of the same sign.
1067-
1064+ >>> npf.mirr([
1065+ ... 100, 50, 60, 70],
1066+ ... 0.10, 0.12,
1067+ ... raise_exceptions=True
1068+ ... ) #doctest: +NORMALIZE_WHITESPACE
1069+ Traceback (most recent call last):
1070+ ...
1071+ numpy_financial._financial.NoRealSolutionError:
1072+ No real solution exists for MIRR since all cashflows are of the same sign.
10681073 """
10691074 values = np .asarray (values )
10701075 n = values .size
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