Skip to content

weather_set

Multi-variable weather file set management.

WeatherSet wraps a dictionary of WeatherVariable -> WeatherData and supports bulk I/O: read/write all four EMOD weather file pairs at once, or convert to/from a single CSV or DataFrame containing all variables.

WeatherSet

A set of weather files for all (or a subset of) EMOD weather variables.

Source code in emodpy_malaria/weather/weather_set.py 
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
class WeatherSet:
    """A set of weather files for all (or a subset of) EMOD weather variables."""

    def __init__(self,
                 dir_path: Union[str, Path] = None,
                 file_names: dict[WeatherVariable, str] = None,
                 weather_columns: dict[WeatherVariable, str] = None):
        self._dir_path: Union[str, Path] = dir_path
        self._file_names: dict[WeatherVariable, str] = file_names or {}
        self._weather_columns: dict[WeatherVariable, str] = weather_columns or {}
        self._weather_dict: dict[WeatherVariable, WeatherData] = {}

    def __getitem__(self, weather_variable: WeatherVariable):
        return self._weather_dict[weather_variable]

    def __setitem__(self, weather_variable: WeatherVariable, weather_object: WeatherData):
        self._weather_dict[weather_variable] = weather_object

    def __len__(self):
        return len(self._weather_dict)

    def __str__(self):
        return str(self.weather_variables)

    def __eq__(self, other):
        if not isinstance(other, WeatherSet):
            return NotImplemented
        if self.weather_variables != other.weather_variables:
            return False
        return all(self[v] == other[v] for v in self.weather_variables)

    def keys(self):
        return self._weather_dict.keys()

    def values(self) -> list[WeatherData]:
        return list(self._weather_dict.values())

    def items(self):
        return self._weather_dict.items()

    @property
    def dir_path(self) -> str:
        return str(self._dir_path)

    @property
    def file_names(self) -> dict[WeatherVariable, str]:
        return self._file_names

    @property
    def attributes(self) -> WeatherAttributes | None:
        if self.weather_variables:
            return self.values()[0].metadata.attributes
        return None

    @property
    def node_ids(self) -> list[int]:
        if self.weather_variables:
            return self.values()[0].metadata.nodes
        return []

    @property
    def id_reference(self) -> str | None:
        if self.weather_variables:
            return self.values()[0].metadata.id_reference
        return None

    @id_reference.setter
    def id_reference(self, value: str) -> None:
        for wd in self.values():
            wd.metadata.id_reference = value

    @property
    def update_resolution(self) -> str | None:
        if self.weather_variables:
            return self.values()[0].metadata.update_resolution
        return None

    @update_resolution.setter
    def update_resolution(self, value: str) -> None:
        for wd in self.values():
            wd.metadata.update_resolution = value

    @property
    def notes(self) -> str | None:
        if self.weather_variables:
            return self.values()[0].metadata.notes
        return None

    @notes.setter
    def notes(self, value: str) -> None:
        for wd in self.values():
            wd.metadata.notes = value

    @property
    def weather_variables(self) -> list[WeatherVariable]:
        return list(self._weather_dict)

    @property
    def weather_columns(self) -> dict[WeatherVariable, str]:
        return self._weather_columns

    # ------------------------------------------------------------------ #
    # CSV / DataFrame I/O
    # ------------------------------------------------------------------ #

    @classmethod
    def from_dataframe(cls, df: pd.DataFrame,
                       node_column: str = None,
                       step_column: str = None,
                       weather_columns: dict[WeatherVariable, str] = None,
                       attributes: WeatherAttributes = None,
                       notes: str = None) -> "WeatherSet":
        """Create from a DataFrame containing all weather variables as columns.

        Args:
            df (pd.DataFrame): DataFrame with node, step, and weather variable columns.
            node_column (str): Column name for node IDs.
            step_column (str): Column name for time steps.
            weather_columns (dict[WeatherVariable, str]): ``{WeatherVariable: column_name}`` mapping.
            attributes (WeatherAttributes): Optional metadata attributes.
            notes (str): Free-text note stored in the weather file metadata.
                Use this to record where the original data came from and
                how it was processed.
        """
        if not isinstance(df, pd.DataFrame):
            raise TypeError(f"Expected DataFrame, got {type(df)}.")
        return cls._from_csv_data(
            data_csv=df, node_column=node_column, step_column=step_column,
            weather_columns=weather_columns, attributes=attributes, notes=notes,
        )

    @classmethod
    def from_csv(cls, file_path: Union[str, Path],
                 node_column: str = None,
                 step_column: str = None,
                 weather_columns: dict[WeatherVariable, str] = None,
                 attributes: WeatherAttributes = None,
                 notes: str = None) -> "WeatherSet":
        """Create from a CSV file containing all weather variables.

        Args:
            file_path (Union[str, Path]): Path to the CSV file.
            node_column (str): Column name for node IDs.
            step_column (str): Column name for time steps.
            weather_columns (dict[WeatherVariable, str]): ``{WeatherVariable: column_name}`` mapping.
            attributes (WeatherAttributes): Optional metadata attributes.
            notes (str): Free-text note stored in the weather file metadata.
                Use this to record where the original data came from and
                how it was processed.
        """
        if not Path(file_path).is_file():
            raise FileNotFoundError(f"CSV file not found: {file_path}")
        return cls._from_csv_data(
            data_csv=str(file_path), node_column=node_column, step_column=step_column,
            weather_columns=weather_columns, attributes=attributes, notes=notes,
        )

    @classmethod
    def _from_csv_data(cls,
                       data_csv: Union[str, pd.DataFrame],
                       node_column: str = None,
                       step_column: str = None,
                       weather_columns: dict[WeatherVariable, str] = None,
                       attributes: WeatherAttributes = None,
                       notes: str = None) -> "WeatherSet":
        infos, weather_columns = cls._init_dataframe_info_dict(node_column, step_column, weather_columns)
        attributes = attributes or WeatherAttributes()
        if notes is not None:
            attributes.notes = notes
        ws = WeatherSet(weather_columns=weather_columns)
        for v, info in infos.items():
            if isinstance(data_csv, str):
                ws[v] = WeatherData.from_csv(file_path=data_csv, info=info, attributes=attributes)
            elif isinstance(data_csv, pd.DataFrame):
                ws[v] = WeatherData.from_dataframe(df=data_csv, info=info, attributes=attributes)
            else:
                raise TypeError(f"Unsupported data type {type(data_csv)}.")
        ws.validate()
        return ws

    def to_dataframe(self,
                     node_column: str = None,
                     step_column: str = None,
                     weather_columns: dict[WeatherVariable, str] = None) -> pd.DataFrame:
        """Export all variables to a single DataFrame."""
        weather_columns = weather_columns or {v: None for v in self.weather_variables}
        not_available = [v for v in weather_columns if v not in self.weather_variables]
        if not_available:
            raise ValueError(f"Requested unavailable weather variables: {not_available}")

        infos, weather_columns = self._init_dataframe_info_dict(node_column, step_column, weather_columns)
        self._weather_columns = weather_columns
        df = None
        for v in infos:
            df2 = self[v].to_dataframe(infos[v])
            if df is None:
                df = df2
            else:
                df[infos[v].value_column] = df2[infos[v].value_column]
        return df

    def to_csv(self, file_path: Union[str, Path],
               node_column: str = None,
               step_column: str = None,
               weather_columns: dict[WeatherVariable, str] = None) -> pd.DataFrame:
        """Export all variables to a single CSV."""
        df = self.to_dataframe(node_column, step_column, weather_columns)
        df.to_csv(file_path, index=False)
        return df

    # ------------------------------------------------------------------ #
    # Binary file I/O
    # ------------------------------------------------------------------ #

    def _load(self) -> "WeatherSet":
        if not self.dir_path or not Path(self.dir_path).is_dir():
            raise ValueError("A valid directory is required.")
        if not self.file_names:
            raise ValueError("File names dictionary is required.")
        for v, n in self.file_names.items():
            bin_path = self._weather_file_path(n)
            self[v] = WeatherData.from_file(bin_path)
        self.validate()
        return self

    def _save(self) -> None:
        if not self._dir_path:
            raise ValueError("Directory is required.")
        if not self._file_names:
            raise ValueError("File names are required.")
        make_path(self._dir_path)
        for v, wd in self._weather_dict.items():
            bin_path = self._weather_file_path(self._file_names[v])
            wd.to_file(bin_path)

    @classmethod
    def from_files(cls, dir_path: Union[str, Path],
                   prefix: str = "",
                   file_names: dict[WeatherVariable, str] = None) -> "WeatherSet":
        """Load from existing ``.bin`` / ``.bin.json`` file pairs in a directory."""
        WeatherVariable.validate_types(file_names, [str, Path])
        file_names = file_names or cls.select_weather_files(dir_path=dir_path, prefix=prefix)
        ws = WeatherSet(dir_path=dir_path, file_names=file_names)
        ws._load()
        return ws

    def to_files(self, dir_path: Union[str, Path],
                 file_names: dict[WeatherVariable, str] = None) -> None:
        """Write all ``.bin`` / ``.bin.json`` file pairs to a directory."""
        file_names = file_names or self.make_file_paths()
        self._dir_path = Path(dir_path)
        self._file_names = file_names
        self._save()

    # ------------------------------------------------------------------ #
    # Helpers
    # ------------------------------------------------------------------ #

    @classmethod
    def _init_weather_columns(cls, weather_columns: dict[WeatherVariable, str | None] = None
                              ) -> dict[WeatherVariable, str]:
        WeatherVariable.validate_types(weather_columns, [str, None])
        if weather_columns:
            weather_variables = list(weather_columns)
        else:
            weather_variables = WeatherVariable.list(exclude=WeatherVariable.LAND_TEMPERATURE)
        weather_columns = weather_columns or {}
        return {v: weather_columns.get(v) or v.value for v in weather_variables}

    @classmethod
    def _init_dataframe_info_dict(cls,
                                  node_column: str = None,
                                  step_column: str = None,
                                  weather_columns: dict[WeatherVariable, str] = None
                                  ) -> tuple[dict[WeatherVariable, DataFrameInfo], dict[WeatherVariable, str]]:
        weather_columns = cls._init_weather_columns(weather_columns)
        info_dict = {
            v: DataFrameInfo(node_column=node_column, step_column=step_column, value_column=weather_columns[v])
            for v in weather_columns
        }
        return info_dict, weather_columns

    @classmethod
    def _make_file_templates(cls,
                             prefix: str = "*",
                             suffix: str = "*{}*.bin",
                             weather_variables: list[WeatherVariable] = None,
                             weather_names: dict[WeatherVariable, str] = None) -> dict[WeatherVariable, str]:
        if prefix is None:
            raise ValueError("Prefix cannot be None.")
        if suffix is None:
            raise ValueError("Suffix cannot be None.")
        WeatherVariable.validate_types(weather_names, [str])

        if not suffix.endswith(".bin") and not suffix.endswith("*"):
            suffix += "*.bin"

        template = prefix + suffix
        template = template.replace("**", "*")

        if not weather_variables:
            weather_variables = WeatherVariable.list(exclude=WeatherVariable.LAND_TEMPERATURE)
        weather_names = weather_names or {v: v.value for v in weather_variables}

        return {v: template.format(weather_names[v]) for v in weather_names}

    @classmethod
    def make_file_paths(cls,
                        dir_path: Union[str, Path] = None,
                        prefix: str = "",
                        suffix: str = "{}.bin",
                        weather_variables: list[WeatherVariable] = None,
                        weather_names: dict[WeatherVariable, str] = None) -> dict[WeatherVariable, str]:
        """Generate conventional EMOD weather file paths."""
        names = cls._make_file_templates(
            prefix=prefix, suffix=suffix,
            weather_names=weather_names, weather_variables=weather_variables,
        )
        if dir_path is not None:
            names = {v: str(Path(dir_path) / n) for v, n in names.items()}
        return names

    @classmethod
    def select_weather_files(cls, dir_path: Union[str, Path],
                             prefix: str = "*",
                             suffix: str = "*{}*.bin",
                             weather_variables: list[WeatherVariable] = None,
                             weather_names: dict[WeatherVariable, str] = None) -> dict[WeatherVariable, str]:
        """Find weather files in a directory by name pattern."""
        if dir_path is None:
            raise ValueError("Directory path is required.")
        templates = cls._make_file_templates(
            prefix=prefix, suffix=suffix,
            weather_names=weather_names, weather_variables=weather_variables,
        )
        names = {}
        for v, pattern in templates.items():
            files = list(Path(dir_path).glob(pattern))
            if len(files) > 1:
                raise ValueError(f"Multiple files match pattern {pattern!r}")
            if len(files) == 1:
                names[v] = files[0].name
        return names

    def _weather_file_path(self, file_name: Union[str, Path]) -> Path:
        return Path(self.dir_path) / str(file_name)

    def validate(self) -> None:
        series_len0 = node_count0 = id_ref0 = resolution0 = years0 = None

        for v, wd in self._weather_dict.items():
            wd.validate()
            wd.metadata.validate()
            wm = wd.metadata

            series_len0 = series_len0 or wm.series_len
            node_count0 = node_count0 or wm.node_count
            id_ref0 = id_ref0 or wm.id_reference
            resolution0 = resolution0 or wm.spatial_resolution
            years0 = years0 or wm.data_years

            label = f" ({self.file_names[v]})" if v in self.file_names else ""
            if series_len0 != wm.series_len:
                raise ValueError(f"series_len mismatch for {v}{label}")
            if node_count0 != wm.node_count:
                raise ValueError(f"node_count mismatch for {v}{label}")
            if id_ref0 != wm.id_reference:
                raise ValueError(f"id_reference mismatch for {v}{label}")
            if resolution0 != wm.spatial_resolution:
                raise ValueError(f"spatial_resolution mismatch for {v}{label}")
            if years0 != wm.data_years:
                raise ValueError(f"data_years mismatch for {v}{label}")

        if self._weather_columns:
            for v in WeatherVariable.list():
                in_data = v in self._weather_dict
                in_cols = v in self._weather_columns
                if in_data != in_cols:
                    raise ValueError(
                        f"Weather variable {v} is in "
                        f"{'data' if in_data else 'columns'} but not "
                        f"{'columns' if in_data else 'data'}."
                    )

from_csv(file_path, node_column=None, step_column=None, weather_columns=None, attributes=None, notes=None) classmethod

Create from a CSV file containing all weather variables.

Parameters:

Name Type Description Default
file_path Union[str, Path]

Path to the CSV file.

 required
node_column str

Column name for node IDs.

 None
step_column str

Column name for time steps.

 None
weather_columns dict[WeatherVariable, str]

{WeatherVariable: column_name} mapping.

 None
attributes WeatherAttributes

Optional metadata attributes.

 None
notes str

Free-text note stored in the weather file metadata. Use this to record where the original data came from and how it was processed.

 None
Source code in emodpy_malaria/weather/weather_set.py 
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
@classmethod
def from_csv(cls, file_path: Union[str, Path],
             node_column: str = None,
             step_column: str = None,
             weather_columns: dict[WeatherVariable, str] = None,
             attributes: WeatherAttributes = None,
             notes: str = None) -> "WeatherSet":
    """Create from a CSV file containing all weather variables.

    Args:
        file_path (Union[str, Path]): Path to the CSV file.
        node_column (str): Column name for node IDs.
        step_column (str): Column name for time steps.
        weather_columns (dict[WeatherVariable, str]): ``{WeatherVariable: column_name}`` mapping.
        attributes (WeatherAttributes): Optional metadata attributes.
        notes (str): Free-text note stored in the weather file metadata.
            Use this to record where the original data came from and
            how it was processed.
    """
    if not Path(file_path).is_file():
        raise FileNotFoundError(f"CSV file not found: {file_path}")
    return cls._from_csv_data(
        data_csv=str(file_path), node_column=node_column, step_column=step_column,
        weather_columns=weather_columns, attributes=attributes, notes=notes,
    )

from_dataframe(df, node_column=None, step_column=None, weather_columns=None, attributes=None, notes=None) classmethod

Create from a DataFrame containing all weather variables as columns.

Parameters:

Name Type Description Default
df DataFrame

DataFrame with node, step, and weather variable columns.

 required
node_column str

Column name for node IDs.

 None
step_column str

Column name for time steps.

 None
weather_columns dict[WeatherVariable, str]

{WeatherVariable: column_name} mapping.

 None
attributes WeatherAttributes

Optional metadata attributes.

 None
notes str

Free-text note stored in the weather file metadata. Use this to record where the original data came from and how it was processed.

 None
Source code in emodpy_malaria/weather/weather_set.py 
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
@classmethod
def from_dataframe(cls, df: pd.DataFrame,
                   node_column: str = None,
                   step_column: str = None,
                   weather_columns: dict[WeatherVariable, str] = None,
                   attributes: WeatherAttributes = None,
                   notes: str = None) -> "WeatherSet":
    """Create from a DataFrame containing all weather variables as columns.

    Args:
        df (pd.DataFrame): DataFrame with node, step, and weather variable columns.
        node_column (str): Column name for node IDs.
        step_column (str): Column name for time steps.
        weather_columns (dict[WeatherVariable, str]): ``{WeatherVariable: column_name}`` mapping.
        attributes (WeatherAttributes): Optional metadata attributes.
        notes (str): Free-text note stored in the weather file metadata.
            Use this to record where the original data came from and
            how it was processed.
    """
    if not isinstance(df, pd.DataFrame):
        raise TypeError(f"Expected DataFrame, got {type(df)}.")
    return cls._from_csv_data(
        data_csv=df, node_column=node_column, step_column=step_column,
        weather_columns=weather_columns, attributes=attributes, notes=notes,
    )

from_files(dir_path, prefix='', file_names=None) classmethod

Load from existing .bin / .bin.json file pairs in a directory.

Source code in emodpy_malaria/weather/weather_set.py 
255
256
257
258
259
260
261
262
263
264
@classmethod
def from_files(cls, dir_path: Union[str, Path],
               prefix: str = "",
               file_names: dict[WeatherVariable, str] = None) -> "WeatherSet":
    """Load from existing ``.bin`` / ``.bin.json`` file pairs in a directory."""
    WeatherVariable.validate_types(file_names, [str, Path])
    file_names = file_names or cls.select_weather_files(dir_path=dir_path, prefix=prefix)
    ws = WeatherSet(dir_path=dir_path, file_names=file_names)
    ws._load()
    return ws

make_file_paths(dir_path=None, prefix='', suffix='{}.bin', weather_variables=None, weather_names=None) classmethod

Generate conventional EMOD weather file paths.

Source code in emodpy_malaria/weather/weather_set.py 
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
@classmethod
def make_file_paths(cls,
                    dir_path: Union[str, Path] = None,
                    prefix: str = "",
                    suffix: str = "{}.bin",
                    weather_variables: list[WeatherVariable] = None,
                    weather_names: dict[WeatherVariable, str] = None) -> dict[WeatherVariable, str]:
    """Generate conventional EMOD weather file paths."""
    names = cls._make_file_templates(
        prefix=prefix, suffix=suffix,
        weather_names=weather_names, weather_variables=weather_variables,
    )
    if dir_path is not None:
        names = {v: str(Path(dir_path) / n) for v, n in names.items()}
    return names

select_weather_files(dir_path, prefix='*', suffix='*{}*.bin', weather_variables=None, weather_names=None) classmethod

Find weather files in a directory by name pattern.

Source code in emodpy_malaria/weather/weather_set.py 
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
@classmethod
def select_weather_files(cls, dir_path: Union[str, Path],
                         prefix: str = "*",
                         suffix: str = "*{}*.bin",
                         weather_variables: list[WeatherVariable] = None,
                         weather_names: dict[WeatherVariable, str] = None) -> dict[WeatherVariable, str]:
    """Find weather files in a directory by name pattern."""
    if dir_path is None:
        raise ValueError("Directory path is required.")
    templates = cls._make_file_templates(
        prefix=prefix, suffix=suffix,
        weather_names=weather_names, weather_variables=weather_variables,
    )
    names = {}
    for v, pattern in templates.items():
        files = list(Path(dir_path).glob(pattern))
        if len(files) > 1:
            raise ValueError(f"Multiple files match pattern {pattern!r}")
        if len(files) == 1:
            names[v] = files[0].name
    return names

to_csv(file_path, node_column=None, step_column=None, weather_columns=None)

Export all variables to a single CSV.

Source code in emodpy_malaria/weather/weather_set.py 
221
222
223
224
225
226
227
228
def to_csv(self, file_path: Union[str, Path],
           node_column: str = None,
           step_column: str = None,
           weather_columns: dict[WeatherVariable, str] = None) -> pd.DataFrame:
    """Export all variables to a single CSV."""
    df = self.to_dataframe(node_column, step_column, weather_columns)
    df.to_csv(file_path, index=False)
    return df

to_dataframe(node_column=None, step_column=None, weather_columns=None)

Export all variables to a single DataFrame.

Source code in emodpy_malaria/weather/weather_set.py 
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
def to_dataframe(self,
                 node_column: str = None,
                 step_column: str = None,
                 weather_columns: dict[WeatherVariable, str] = None) -> pd.DataFrame:
    """Export all variables to a single DataFrame."""
    weather_columns = weather_columns or {v: None for v in self.weather_variables}
    not_available = [v for v in weather_columns if v not in self.weather_variables]
    if not_available:
        raise ValueError(f"Requested unavailable weather variables: {not_available}")

    infos, weather_columns = self._init_dataframe_info_dict(node_column, step_column, weather_columns)
    self._weather_columns = weather_columns
    df = None
    for v in infos:
        df2 = self[v].to_dataframe(infos[v])
        if df is None:
            df = df2
        else:
            df[infos[v].value_column] = df2[infos[v].value_column]
    return df

to_files(dir_path, file_names=None)

Write all .bin / .bin.json file pairs to a directory.

Source code in emodpy_malaria/weather/weather_set.py 
266
267
268
269
270
271
272
def to_files(self, dir_path: Union[str, Path],
             file_names: dict[WeatherVariable, str] = None) -> None:
    """Write all ``.bin`` / ``.bin.json`` file pairs to a directory."""
    file_names = file_names or self.make_file_paths()
    self._dir_path = Path(dir_path)
    self._file_names = file_names
    self._save()