> For the complete documentation index, see [llms.txt](https://eflow.gitbook.io/ffc-readme/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://eflow.gitbook.io/ffc-readme/functional-flow-calculator/metrics/wet-season/wet-season-timing.md).

# Timing

## Definition

The start timing of the wet season baseflow period is defined as the date that sufficient baseflow has accrued based on a magnitude threshold and a rate of change threshold. This metric is measured in Julian days, where January 1st = 1 and December 31st = 365.

## Steps

1. Convert raw date and flow data columns into a flow matrix based on water year. Each column starts with the beginning of the water year (i.e. 10/1) and ends with the end of the water year (i.e.9/30).
2. Check if the data contains more than the allowed None and zero data. Then, interpolate between missing values to get a full dataset:

   ```python
   if np.isnan(flow_matrix[:, column_number]).sum() > max_nan_allowed_per_year or np.count_nonzero(flow_matrix[:, column_number]==0) > max_zero_allowed_per_year:
         continue
   ```
3. Filter the raw flow data with a broad resolution (high sigma) and a finer resolution (low sigma) filter to use in peak identifications:

   ```python
   wet_season_filter_data = gaussian_filter1d(flow_data, wet_season_sigma)
   broad_filter_data = gaussian_filter1d(flow_data, broad_sigma)
   ```
4. Identify the max and min flow values of the high sigma filtered data, and identify an array of peaks using the low sigma filtered data. Max flow is searched for starting 20 days after the beginning of the water year. The min flow is then found in the range from the beginning of the water year until the timing of the max flow. 

   ```python
    max_wet_peak_mag = max(broad_filter_data[20:])
    max_wet_peak_index = find_index(broad_filter_data, max_wet_peak_mag)
    min_wet_peak_mag = min(broad_filter_data[:max_wet_peak_index])
    maxarray_wet, _ = peakdet(wet_season_filter_data, peak_sensitivity_wet)
   ```
5. Identify the earliest peak of sufficient relative magnitude to use as the search index for the start of the wet season.

   ```python
   if (maxarray_wet[index][1]-min_wet_peak_mag)/(max_wet_peak_mag-min_wet_peak_mag) > peak_detect_perc:
       search_index = int(maxarray_wet[index][0])
       break
   ```
6. Search from right to left, starting at the search index, for the first flow value that falls below the relative magnitude threshold of 20% and below a user-defined slope threshold. This flow date is set as the start date of the wet season.

   ```python
   for index, value in enumerate(reversed(wet_season_filter_data[:search_index])):
        if index == len(wet_season_filter_data[:search_index] - 1):
            return None
        elif (value - min_wet_peak_mag) / (max_wet_peak_mag - min_wet_peak_mag) < wet_threshold_perc and abs(spl_first(search_index - index)) < max_wet_peak_mag/slope_sensitivity:
            """If value percentage falls below wet_threshold_perc"""
            return_date = search_index - index
            return return_date
   ```