swirlspy.ver package
Submodules
swirlspy.ver.gen_contingency module
- swirlspy.ver.gen_contingency.contingency(threshold, forecast, observed)
Generates contingency table statistics for traditional binary verification
- threshold: float
Threshold value of variable for verification.
- forecast: xarray
An xarray containing forecasted values of variable
- observed: xarray
An xarray containing observed values of variable
- Returns
contingency – tuple structure is (hit, miss, false_alarm, corrneg)
- Return type
tuple
Notes
- hit: int
number of hits
- miss: int
number of misses
- false_alarm: int
number of false alarms
- corrneg: int
number of correct negatives
swirlspy.ver.gen_metrics module
- swirlspy.ver.gen_metrics.accuracy(hit, miss, false_alarm, corrneg)
Calculates accuracy
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
corrneg (int) – number of correct negatives
- Returns
accuracy – accuracy = (hit+corrneg)/total total = hit+miss+false_alarm+corrneg
- Return type
float
- swirlspy.ver.gen_metrics.csi(hit, miss, false_alarm)
Calculates Critical Success Index
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
- Returns
critical success index (float)
csi = hit/(hit+false_alarm+miss)
- swirlspy.ver.gen_metrics.ets(hit, miss, false_alarm, corrneg)
Returns Equitable Threat Score[0 = no skill][1 = perfect]
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
corrneg (int) – number of correct negatives
- Returns
equitable threat score (float)
ets = (hit - hit_expected)/(hit+miss+false_alarm-hit_expected)
where hit_expected = hit+miss)*(hit+false_alarm)/total
total = hit + miss + false_alarm + corrneg
- swirlspy.ver.gen_metrics.far(hit, false_alarm)
Calculates False Alarm Ratio
- Parameters
hit (int) – number of hits
false_alarm (int) – number of false alarms
- Returns
false alarm ratio – far = false_alarm/(hit+false_alarm)
- Return type
float
- swirlspy.ver.gen_metrics.freq_bias(hit, miss, false_alarm)
Calculates Frequency Bias
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
- Returns
frequency bias (float or int)
freq_bias = (hit+false_alarm)/(hit+miss)
- swirlspy.ver.gen_metrics.hss(hit, miss, false_alarm, corrneg)
Calculates Heidke Skill Score [0 = no skill][1=perfect]
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
corrneg (int) – number of correct negatives
- Returns
Heidke Success Score – hss = (hit+corrneg-hit_corrneg_expected)/(total-hit_corrneg_expected) hit_corrneg_expected = ((hit+miss)*(hit+false_alarm)
(false_alarm+corrneg) * (miss+corrneg)) / total
- Return type
int or float
- swirlspy.ver.gen_metrics.pod(hit, miss)
Calculates Probability of Detection
- Parameters
hit (int) – number of hits
miss (int) – number of misses
- Returns
probability of detection – pod = hit/(hit+miss)
- Return type
float
- swirlspy.ver.gen_metrics.pofd(false_alarm, corrneg)
Calculates Probability of False Detection
- Parameters
hit (int) – number of hits
miss (int) – number of misses
false_alarm (int) – number of false alarms
corrneg (int) – number of correct negatives
- Returns
probability of false detection – pofd = false_alarm/(false_alarm + corrneg)
- Return type
float