document new features on readme

Readme.md

@@ -33,13 +33,15 @@ pip install hecdss
 ### Regular TimeSeries Data
 - **Stored object in Python**: Regular Timeseries data is stored as 2 arrays of values and times/interval and startdate.
 - **Attributes**: The TimeSeries object has the following attributes:
-  - `start_date`: The start date of the time series data.
-  - `times`: The times of the time series data.
-  - `values`: The values of the time series data.
-  - `interval`: The interval of the time series data.
-  - `data_type`: the dss data type ("PER-AVER","PER-CUM", "INST-VAL", "INST-CUM", .. )
-  - `units`: The units of the time series data.
-  - `id`: The Path of the time series data.
+  - `start_date (str)`: The start date of the time series data.
+  - `times (list[datetime])`: The times of the time series data.
+  - `values (nd.nparray[float])`: The values of the time series data.
+  - `quality (list[int])`: The quality flags of the time series data.
+  - `notes (list[str])`: The notes of the time series data. 
+  - `interval (str)`: The interval of the time series data.
+  - `data_type (str)`: the dss data type ("PER-AVER","PER-CUM", "INST-VAL", "INST-CUM", .. )
+  - `units (str)`: The units of the time series data.
+  - `id (str)`: The Path of the time series data (Format: "/A/B/C/D/E/F/").
 
 ```python
 # example working with time-series data
@@ -60,21 +62,22 @@ with HecDss(file_path) as dss:
 ### Irregular TimeSeries Data
 - **Stored object in Python**: Irregular Timeseries data is stored as 2 arrays of values and times.
 - **Attributes**: The TimeSeries object has the following attributes:
-  - `times`: The times of the time series data.
-  - `values`: The values of the time series data.
+  - `times (list[datetime])`: The times of the time series data.
+  - `values (np.ndarray[float])`: The values of the time series data.
   - `units`: The units of the time series data.
-  - `data_type`: the dss data type ('INST-VAL', 'INST-CUM')
-  - `data`: The time series data.
-  - `id`: The Path of the time series data.
+  - `quality (list[int])`: The quality flags of the time series data.
+  - `notes (list[str])`: The notes of the time series data. 
+  - `data_type (str)`: the dss data type ('INST-VAL', 'INST-CUM')
+  - `id (str)`: The Path of the time series data (Format: "/A/B/C/D/E/F/").
 
 
 ### Paired Data
 - **Stored object in Python**: Paired data stored as aa (x, y) where y could be stored as a 2d numpy matrix.
 - **Attributes**: The PairedData object has the following attributes:
-  - `ordinates`: The x values of the paired data
-  - `values`: y values of the paired data stored as 2d numpy array.
-  - `labels`: The labels of the paired data.
-  - `id`: The Path of the paired data.
+  - `ordinates (np.ndarray)`: The x values of the paired data
+  - `values (np.ndarray[float])`: y values of the paired data stored as 2d numpy array.
+  - `labels (list[str])`: The labels of the paired data.
+  - `id (str)`: The Path of the paired data.
 
 ### Gridded Data
 - **Stored object in Python**: A 2d matrix stored as a numpy 2d object.
@@ -87,6 +90,7 @@ with HecDss(file_path) as dss:
 - * Supports storing and reading arrays of integers, floats, or doubles.
 - * Arrays are managed with ArrayContainer
 
+
 ```python
   # Example working with an array
   with HecDss("my-dss-file.dss") as dss:
@@ -98,8 +102,33 @@ with HecDss(file_path) as dss:
     read_array = dss.get(array_ints.id)
 ```
 
+## CSV Functionality
+
+### Time Series
+The `rts.to_csv` and `RegularTimeSeries.read_csv` methods can be used to convert time series data to `.csv` format. This can also be utilized alongside `pandas` methods to convert time series data to pandas DataFrames.
 
+```python
+  # Round trip CSV example with RegularTimeSeries and Pandas DataFrame
+  import pandas as pd
+
+  file_path: str = "my-dss-file.dss"
+  with HecDss(file_path) as dss:
+    data_path: str = "/example/data/////"
+    rts: RegularTimeSeries = dss.get(data_path)
+
+    csv_out: str = "example.csv"
+    rts.to_csv(csv_out) # RegularTimeSeries -> CSV
+
+  df: pd.DataFrame = pd.read_csv(csv_out) # CSV -> DataFrame
+  df_csv_path: str = "df_example.csv"
+  # Index needs to be false for round-trip support
+  df.to_csv(df_csv_path, index=False) # DataFrame -> CSV
+
+  with HecDss(file_path) as dss:
+    RegularTimeSeries.read_csv(df_csv_path) # CSV -> RegularTimeSeries
+```
 
+Additionally, `IrregularTimeSeries` and `PairedData` both have equivalent functionality with `.csv` conversion.
 
 
 ### This libray is built using the API for future versions of HEC-DSS 
@@ -153,7 +182,6 @@ These are the driving design ideas and goals of the hec-dss-python project (subj
 | hec_dss_native.py | native binding layer   | isolate interactions with low level library(if performance is an issue this Ctypes layer can be replaced )       |
 | hec_dss.py | Programmer entry point ; Python API  | Hides interactions with hec_dss_native, seek to be simple user experience                                        |
 |catalog.py|manage list of DSS objects (catalog) | create condensed catalog perspective                                                                             |
-|Pandas_Series_Utilities.py [future](https://github.com/HydrologicEngineeringCenter/hec-dss-python/issues/8) |NumPy/pandas support | provide features such as dataframes, separate from hec-dss.py; can be developed by different/parallel developers |
 |Easy to get started |nothing to install, just copy python files and shared library   | require minimal privileges to install                                                                            | 
 
 

src/hecdss/regular_timeseries.py

@@ -26,7 +26,9 @@ def __init__(self):
         self.id = ""
         self.location_info = None
 
-    def add_data_point(self, date: datetime, value: float, flag: int = None, note: str = None):
+    def add_data_point(
+        self, date: datetime, value: float, flag: int = None, note: str = None
+    ):
         """
         Adds a data point to the time series.
 
@@ -117,6 +119,7 @@ def to_csv(self, file_path: str) -> None:
             file_path (str): The path to the .csv file where the data will be exported.
         """
         from .dss_csv import timeseries_to_csv
+
         timeseries_to_csv(self, file_path)
         print(f"Wrote RegularTimeSeries to .csv file at {file_path}.")
 
@@ -184,9 +187,17 @@ def _interval_to_times(self, new_interval):
         Args:
             new_interval (int): The new interval in seconds.
         """
-        def is_leap(y): return y % 4 == 0 and y % 100 != 0 or y % 400 == 0
-        def last_day(y, m): return 31 if m in (1, 3, 5, 7, 8, 10, 12) else 30 if m in (
-            4, 6, 9, 11) else 29 if is_leap(y) else 28
+
+        def is_leap(y):
+            return y % 4 == 0 and y % 100 != 0 or y % 400 == 0
+
+        def last_day(y, m):
+            return (
+                31
+                if m in (1, 3, 5, 7, 8, 10, 12)
+                else 30 if m in (4, 6, 9, 11) else 29 if is_leap(y) else 28
+            )
+
         if type(self.start_date) == datetime:
             tz = ZoneInfo(self.time_zone_name) if self.time_zone_name else None
             first_time = self.start_date.replace(microsecond=0, tzinfo=tz)
@@ -238,12 +249,16 @@ def _generate_times(self):
         """
         Generates times for the time series based on the interval and start date.
         """
-        if (len(self.times) > 0 and self.start_date == ""):
+        if len(self.times) > 0 and self.start_date == "":
             self.start_date = self.times[0]
 
-        x = [self._get_interval_times(), self._get_interval_path(), self._get_interval_interval()]
+        x = [
+            self._get_interval_times(),
+            self._get_interval_path(),
+            self._get_interval_interval(),
+        ]
         x = [i for i in x if i != "empty"]
-        if (not all(i == x[0] for i in x)):
+        if not all(i == x[0] for i in x):
             raise ValueError("inconsistent interval within arguments")
         elif len(x) != 3 and len(x) != 0:
             self._interval_to_interval(x[0])
@@ -262,17 +277,33 @@ def read_csv(file_path: str) -> "RegularTimeSeries":
             RegularTimeSeries: A new instance of RegularTimeSeries populated with the data from the .csv file.
         """
         from .dss_csv import timeseries_read_csv
+
         return timeseries_read_csv(RegularTimeSeries, file_path)
 
     @staticmethod
-    def create(values, times=[], quality=[], notes=[], units="", data_type="", interval="", start_date="", time_granularity_seconds=1, julian_base_date=0, time_zone_name="", path=None, location_info=None):
+    def create(
+        values,
+        times=[],
+        quality=[],
+        notes=[],
+        units="",
+        data_type="",
+        interval="",
+        start_date="",
+        time_granularity_seconds=1,
+        julian_base_date=0,
+        time_zone_name="",
+        path=None,
+        location_info=None,
+    ):
         """
         Creates a new instance of the RegularTimeSeries class with the specified parameters.
 
         Args:
             values (list): List of data values.
             times (list, optional): List of time values. Defaults to [].
             quality (list, optional): List of quality values. Defaults to [].
+            notes (list[str]): The notes of the time series data.
             units (str, optional): Units of the data. Defaults to "".
             data_type (str, optional): Type of the data. Defaults to "".
             interval (str, optional): Interval of the time series. Defaults to "".